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318 articles · Page 1 of 4
Architect
Architect
Sep 28, 2026 · Artificial Intelligence

Dream-RSI: How Agents 'Dream' on Past Searches to Optimize Exploration Budgets

Dream-RSI introduces a recursive self-improvement framework where AI agents improve exploration strategies by replaying historical search trees offline, enabling budget allocation decisions without retraining models, demonstrated across algorithm engineering, math optimization, and GPU kernel tasks with reduced search costs.

AI researchDream-RSIGoogle DeepMind
0 likes · 26 min read
Dream-RSI: How Agents 'Dream' on Past Searches to Optimize Exploration Budgets
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Aug 30, 2026 · Artificial Intelligence

How Knowledge Editing Enables Continual Learning in Large Language Models

The article analyzes how large language models can move beyond temporary context or retrieval tricks by using knowledge editing to modify internal parameters, forming distributed knowledge loops, confidence‑guided belief updates, and a self‑sustaining continual‑learning cycle, while also serving as a scientific tool for probing model mechanisms.

AI researchContinual LearningKnowledge Editing
0 likes · 14 min read
How Knowledge Editing Enables Continual Learning in Large Language Models
AI Info Trend
AI Info Trend
Aug 27, 2026 · Artificial Intelligence

AI Knows What You're Trying to Solve: The Rise of the Human Problem Database

Anthropic's research reveals AI companies are accumulating unprecedented 'human problem databases' through conversations, enabling external analysis via privacy-preserving frameworks while raising questions about data control, AI's role in high-stakes decisions, and the shift from search intent to problem intent as a more valuable signal for society and business.

AI ethicsAI productivityAI research
0 likes · 19 min read
AI Knows What You're Trying to Solve: The Rise of the Human Problem Database
Machine Heart
Machine Heart
Aug 21, 2026 · Artificial Intelligence

Bohr Science Space Automates Research ‘Heavy Lifting’ to Free Scientists’ Time

Bohr Science Space provides a desktop‑integrated AI research assistant that automates literature mining, hypothesis generation, experiment design, computation, data analysis, and manuscript drafting, enabling scientists to focus on creative questions while the platform’s multimodal knowledge engine, SciX orchestration, and domain‑specific masters (BioMaster, PharmMaster, MatMaster) deliver end‑to‑end workflow support with reported accuracies above 98% and a 98.8% task success rate.

AI researchDrug discoveryMaterials science
0 likes · 13 min read
Bohr Science Space Automates Research ‘Heavy Lifting’ to Free Scientists’ Time
Architect Practice
Architect Practice
Aug 20, 2026 · Artificial Intelligence

From a World Championship Win to ChatGPT: What OpenAI Got Right in Seven Years

The article traces OpenAI’s seven‑year journey from the OpenAI Five Dota 2 victory to ChatGPT, showing how a clear goal, massive self‑play, scaling of compute and data, and systematic transfer of learned capabilities enabled the transition from game‑playing AI to a widely used conversational product.

AI researchChatGPTDota 2
0 likes · 22 min read
From a World Championship Win to ChatGPT: What OpenAI Got Right in Seven Years
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Aug 17, 2026 · Artificial Intelligence

Can AI Really Self‑Evolve? MLS‑Bench Reveals Limits of Kimi K3 and Qwen3.8‑Max

The MLS‑Bench benchmark evaluates 140 real research tasks across 12 domains, showing that while models like Kimi K3 and Qwen3.8‑Max can boost scores through multi‑round optimization, they rarely discover genuinely new methods or demonstrate reliable experimental planning under flexible compute budgets.

AI researchBenchmarkMLS‑Bench
0 likes · 18 min read
Can AI Really Self‑Evolve? MLS‑Bench Reveals Limits of Kimi K3 and Qwen3.8‑Max
Machine Heart
Machine Heart
Aug 15, 2026 · Artificial Intelligence

Polaris: An Open‑Source AI Research Agent that Partners with You

Polaris is an open‑source, end‑to‑end AI research assistant from Zhejiang University that automates literature review, idea generation, experiment execution, and paper writing, while keeping humans in the loop for critical decisions, and offers a collaborative platform with extensible skills and external tool integration.

AI assistantAI researchPolaris
0 likes · 10 min read
Polaris: An Open‑Source AI Research Agent that Partners with You
java1234
java1234
Aug 14, 2026 · Artificial Intelligence

Agentic AI Boom 2026: Insights from 322 Top Conference Papers

The article highlights the rapid surge of agentic AI research in 2026—arXiv shows about 9,000 papers with 99% published after 2023, monthly additions of ~1,000, a three‑fold yearly increase, 24% of ICML2026 workshops focused on agents, and a curated list of 322 top papers plus practical modules, while warning against over‑reliance on AI tools.

AI researchAI safetyAI tools
0 likes · 5 min read
Agentic AI Boom 2026: Insights from 322 Top Conference Papers
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Aug 11, 2026 · Artificial Intelligence

Bengio Team’s ERRLESS Boosts Symbolic Regression with 10× Faster Posterior Sampling

The paper introduces ERRLESS, a Bayesian symbolic regression framework that reformulates posterior sampling as a maximum‑entropy reinforcement‑learning problem, achieving ten‑fold speedups, robust noise handling, and state‑of‑the‑art performance on Feynman and Blackbox benchmarks.

AI researchGFlowNetReinforcement Learning
0 likes · 10 min read
Bengio Team’s ERRLESS Boosts Symbolic Regression with 10× Faster Posterior Sampling
PaperAgent
PaperAgent
Aug 6, 2026 · Artificial Intelligence

What Research Directions Are Worth Pursuing After Reviewing 407 Large Model Papers?

The author curates a collection of 407 recent large‑model papers—264 frontier works across six innovation paths and 143 top‑conference papers—classifies them into 14 hot sub‑topics, and explains how labs can match these directions to their available compute, data, and time resources.

AI researchMultimodalRetrieval-Augmented Generation
0 likes · 4 min read
What Research Directions Are Worth Pursuing After Reviewing 407 Large Model Papers?
SuanNi
SuanNi
Aug 5, 2026 · Artificial Intelligence

Qwen3.8-Max: Open‑Source Max‑Level LLM That Automates Programming, Office Work, and Research

Qwen3.8-Max, a 2.4 trillion‑parameter open‑weight LLM, ranks fourth on Frontend Code Arena, autonomously completes multi‑day coding projects, reproduces and surpasses a research paper, dominates a multimodal dialogue contest, and tackles real‑world office, chip‑design, and e‑commerce tasks, showcasing unprecedented self‑directed capability.

AI researchBenchmarkMultimodal
0 likes · 11 min read
Qwen3.8-Max: Open‑Source Max‑Level LLM That Automates Programming, Office Work, and Research
Machine Heart
Machine Heart
Aug 2, 2026 · Artificial Intelligence

Can a Single GPU Yield the Best Sub‑100M Parameter Language Model?

BarunLM‑35M, a 35‑million‑parameter language model trained on a single H200 GPU, achieves a 41.01% average score on nine zero‑shot benchmarks, surpassing much larger models, thanks to a hybrid local‑global attention scheme, learnable residual selector, and other architectural optimizations.

AI researchBarunLM-35Mlocal-global attention
0 likes · 6 min read
Can a Single GPU Yield the Best Sub‑100M Parameter Language Model?
Machine Heart
Machine Heart
Jul 29, 2026 · Artificial Intelligence

How Shanghai AI Lab Is Turning Pre‑training Into a Fully Open, Transparent Process

Shanghai AI Lab’s Shushu team launches ArchSpace, an open‑source platform that releases full model checkpoints, training logs, architecture decisions and validation results, inviting the community to submit and verify foundation‑model innovations such as Inter‑layer Attention and Sparse Attention across 1B‑8B scale models, with transparent logs on W&B and HuggingFace.

AI researchArchSpaceCommunity Validation
0 likes · 10 min read
How Shanghai AI Lab Is Turning Pre‑training Into a Fully Open, Transparent Process
Machine Heart
Machine Heart
Jul 26, 2026 · Industry Insights

Why Are Leading AI Companies Competing for Top Scientists?

The article analyzes the accelerating migration of elite scientists—including a Fields Medalist—to top AI firms like OpenAI, Anthropic, and ByteDance, highlighting how these companies are reshaping research priorities, benchmark practices, and long‑term AI development strategies.

AI researchByteDanceOpenAI
0 likes · 11 min read
Why Are Leading AI Companies Competing for Top Scientists?
360 Tech Engineering
360 Tech Engineering
Jul 21, 2026 · Artificial Intelligence

Six 360 AI Institute Papers Accepted at 2026 Top AI Conferences Signal a Shift Toward Precise, Controllable AI

In the first half of 2026, 360 AI Institute saw six papers accepted at ICLR, CVPR, ICML and ECCV, covering multimodal understanding, agents, image generation and editing, while Chinese contributions reached 43.7% of ICLR submissions, highlighting a global move toward more precise and controllable artificial intelligence.

AI researchAgentsMultimodal
0 likes · 9 min read
Six 360 AI Institute Papers Accepted at 2026 Top AI Conferences Signal a Shift Toward Precise, Controllable AI
Machine Heart
Machine Heart
Jul 21, 2026 · Artificial Intelligence

Can Multi-Agent Systems Be Built Like LEGO? Introducing Agent Primitives for Modular Reuse

The paper proposes Agent Primitives—reusable latent building blocks for multi‑agent systems—that replace hand‑crafted pipelines with modular collaboration patterns, communicate via KV‑Cache to avoid natural‑language bottlenecks, and demonstrate significant accuracy, efficiency, and stability gains across diverse tasks and LLM backbones.

AI researchKV CacheLLM communication
0 likes · 12 min read
Can Multi-Agent Systems Be Built Like LEGO? Introducing Agent Primitives for Modular Reuse
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Jul 18, 2026 · Artificial Intelligence

Why Large Language Models Need a ‘Sleep’ Phase to Overcome Forgetting

Google researchers propose a ‘sleep’ stage for large language models, where after active use the model consolidates recent experiences through knowledge‑seeding and self‑generated ‘dreaming’ tasks, addressing catastrophic forgetting and enabling continual learning.

AI researchContinual Learningcatastrophic forgetting
0 likes · 11 min read
Why Large Language Models Need a ‘Sleep’ Phase to Overcome Forgetting
ThinkingAgent
ThinkingAgent
Jul 13, 2026 · Industry Insights

Global AI Talent Map: 8.4 Million Engineers, Distribution, Mobility & Competition

The article analyses the global AI talent ecosystem, revealing a pyramid of 8.4 million skill‑holders, the rise of China as a top‑researcher producer, geographic concentration in small nations, shifting migration patterns away from the US, salary differentials, policy battles and the challenges facing China’s talent retention.

AI policyAI researchAI talent
0 likes · 20 min read
Global AI Talent Map: 8.4 Million Engineers, Distribution, Mobility & Competition
Machine Heart
Machine Heart
Jul 11, 2026 · Industry Insights

When a NeurIPS Paper Lands at ICML: The Poster War at Top AI Conferences

The article examines how the poster session at ICML has turned into a competitive showcase, highlighting creative tactics like T‑shirt displays, merch giveaways, hand‑drawn posters, and eye‑catching titles, while offering practical design advice amid a flood of over 6,000 submissions.

AI researchAcademic ConferenceConference Culture
0 likes · 5 min read
When a NeurIPS Paper Lands at ICML: The Poster War at Top AI Conferences
Data Party THU
Data Party THU
Jun 30, 2026 · Artificial Intelligence

Large-Scale Sign Language Datasets: Resources, Benchmarks, and Annotation Standards

This ACL 2026 survey systematically reviews over 120 publicly available sign‑language datasets covering 35 languages, analyzes their modalities, annotation inconsistencies, and benchmark limitations, and proposes a 24‑field datasheet to promote reproducible and comparable AI research in sign language recognition, translation, and generation.

AI researchBenchmarksMultimodal
0 likes · 15 min read
Large-Scale Sign Language Datasets: Resources, Benchmarks, and Annotation Standards
Machine Heart
Machine Heart
Jun 30, 2026 · Artificial Intelligence

LiveWorld: A New Paradigm for Video World Models that Keeps Off‑Screen Worlds Evolving

LiveWorld introduces a novel video world modeling paradigm that explicitly separates world evolution from observation rendering, enabling objects and events to continue evolving even when they leave the camera view; extensive experiments on the new LiveBench benchmark show substantial gains over prior camera‑controllable models.

AI researchBenchmarkLiveWorld
0 likes · 13 min read
LiveWorld: A New Paradigm for Video World Models that Keeps Off‑Screen Worlds Evolving
Machine Heart
Machine Heart
Jun 28, 2026 · Industry Insights

Where Have the Eight Transformers' Pioneers Ended Up?

The article traces the post‑Google journeys of the eight "Attention Is All You Need" authors, detailing recent high‑profile exits to OpenAI and Anthropic, market fallout, each researcher’s contributions to the Transformer architecture, and how their divergent paths continue to shape AI beyond the original paper.

AI researchEssential AIGoogle DeepMind
0 likes · 21 min read
Where Have the Eight Transformers' Pioneers Ended Up?
Machine Heart
Machine Heart
Jun 26, 2026 · Industry Insights

Dawn Song, Leading Computer Security Expert, Joins Meta’s Superintelligence Labs

Dawn Song, a world‑renowned computer security and AI safety scholar and UC Berkeley professor, has become Meta’s VP of AI research, bringing her award‑winning work—including Dynamic Taint Analysis and the ALE benchmark—and her startups Oasis Labs and Virtue AI to strengthen Meta’s agent‑centric safety strategy.

AI researchAI safetyALE benchmark
0 likes · 5 min read
Dawn Song, Leading Computer Security Expert, Joins Meta’s Superintelligence Labs
Machine Heart
Machine Heart
Jun 25, 2026 · Industry Insights

Why DeepMind Veterans Are Leaving London: The Ongoing AI Talent Drain at Google

Top DeepMind researchers including Jonas Adler, Alexander Pritzel and Arthur Conmy are departing Google for Anthropic, highlighting a shift from Google's research‑lab culture to a model‑factory focus, a geographic move from London to Mountain View, and growing talent competition in the AI industry.

AI researchAI talentAnthropic
0 likes · 8 min read
Why DeepMind Veterans Are Leaving London: The Ongoing AI Talent Drain at Google
PaperAgent
PaperAgent
Jun 23, 2026 · Artificial Intelligence

Arbor Boosts Autonomous Research Performance 150% Over Claude Code

Arbor, a collaborative framework from RUC and Microsoft, uses Hypothesis‑Tree Refinement to turn short‑lived experiments into lasting research progress, achieving over 2.5× held‑out gains across six autonomous optimization tasks and setting a new SOTA on MLE‑Bench Lite.

AI researchArborAutonomous Optimization
0 likes · 10 min read
Arbor Boosts Autonomous Research Performance 150% Over Claude Code
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Jun 22, 2026 · Interview Experience

Avoid Job‑Hunting Pitfalls: How a NLP PhD Secured an OpenAI Offer After 57 Interviews

Alisa Liu, a six‑year NLP PhD, shares a step‑by‑step recount of her job hunt—57 interviews across 11 top AI firms, including OpenAI—detailing interview formats, preparation tactics, offer negotiation, and the emotional toll, offering a practical guide to avoid common pitfalls for future candidates.

AI researchBehavioral InterviewInterview Preparation
0 likes · 12 min read
Avoid Job‑Hunting Pitfalls: How a NLP PhD Secured an OpenAI Offer After 57 Interviews
Machine Heart
Machine Heart
Jun 22, 2026 · Artificial Intelligence

Why Dropping VAE and Private Data Boosts Text-to-Image Generation Performance

MiniT2I, a minimalist pixel-space text-to-image model that discards VAE, AdaLN, and private data, achieves 0.87 GenEval and 84.2 DPG-Bench scores with only 258 M parameters, demonstrating that a stripped-down architecture and public data can outperform larger, more complex systems.

AI researchMiniT2ITransformer
0 likes · 8 min read
Why Dropping VAE and Private Data Boosts Text-to-Image Generation Performance
SuanNi
SuanNi
Jun 17, 2026 · Artificial Intelligence

Can a 3B Small Model Match Top Closed‑Source LLMs? VibeThinker-3B’s Limits

VibeThinker-3B, a newly open‑sourced 3‑billion‑parameter model, achieves near‑state‑of‑the‑art scores on math competitions (AIME, IMO‑AnswerBench), coding (LiveCodeBench), and verification benchmarks, rivaling trillion‑parameter closed models, thanks to a Spectrum‑to‑Signal training pipeline, multi‑stage SFT, RL, and offline distillation, supporting a new parametric compression‑coverage hypothesis.

AI researchbenchmarkinginference
0 likes · 8 min read
Can a 3B Small Model Match Top Closed‑Source LLMs? VibeThinker-3B’s Limits
Machine Heart
Machine Heart
Jun 17, 2026 · Artificial Intelligence

Can a 3B Model Rival Opus 4.5 in Programming? Inside the Domestic VibeThinker‑3B

VibeThinker‑3B, a 3‑billion‑parameter Chinese‑built model, achieves programming benchmark scores comparable to top‑tier models like Opus 4.5, excelling in AIME, HMMT, LiveCodeBench and LeetCode contests, thanks to its Spectrum‑to‑Signal training pipeline, Claim‑Level reliability evaluation, and multi‑stage SFT and RL refinements.

AI researchClaim-Level ReliabilitySpectrum-to-Signal
0 likes · 7 min read
Can a 3B Model Rival Opus 4.5 in Programming? Inside the Domestic VibeThinker‑3B
Machine Heart
Machine Heart
Jun 17, 2026 · Artificial Intelligence

Why Transformers Struggle with State Tracking and How Recurrence Could Fix It

The DeepMind paper “The Topological Trouble With Transformers” reveals that the Transformer architecture inherently fails at state tracking, making chain‑of‑thought prompting only a costly patch, and proposes returning to recurrent mechanisms—such as looped or sequence‑wise recurrence—to achieve true, continuous memory.

AI researchDeepMindRecurrent Models
0 likes · 9 min read
Why Transformers Struggle with State Tracking and How Recurrence Could Fix It
Machine Heart
Machine Heart
Jun 15, 2026 · R&D Management

How to Become an Outstanding AI Researcher: Lessons from an Anthropic Scientist

The article distills an Anthropic researcher’s candid guide on becoming a truly effective AI researcher, emphasizing deliberate practice of small skills—topic selection, literature reading, writing, rapid experiment cycles—and drawing on historic insights from Hamming, Sutton, Shannon, and others.

AI researchacademic writinghistorical insights
0 likes · 14 min read
How to Become an Outstanding AI Researcher: Lessons from an Anthropic Scientist
AI Architecture Path
AI Architecture Path
Jun 12, 2026 · Artificial Intelligence

How a New AI Research Skill Gained 2,685 Stars in One Day and Helps Anyone Bridge the Information Gap

The article explains how the open‑source tool last30days‑skill outperforms traditional search by aggregating real‑time community consensus from over 14 platforms—including Reddit, X, YouTube, and Polymarket—into structured, source‑backed reports, and provides detailed installation, configuration, and use‑case guidance for creators, product teams, developers, and investors.

AI researchPolymarketcommunity consensus
0 likes · 17 min read
How a New AI Research Skill Gained 2,685 Stars in One Day and Helps Anyone Bridge the Information Gap
Top Architect
Top Architect
Jun 11, 2026 · Artificial Intelligence

Gemini Omni Review: How One Prompt Turns Sketches into Cinematic Videos

Google DeepMind’s Gemini Omni is presented as a new world model that combines reasoning and generation to enable conversational video editing, multimodal training, and emergent capabilities, contrasting it with Veo while discussing trade‑offs, safety measures, and the model’s broader impact on AI development.

AI researchGemini Omniemergent behavior
0 likes · 10 min read
Gemini Omni Review: How One Prompt Turns Sketches into Cinematic Videos
HyperAI Super Neural
HyperAI Super Neural
Jun 11, 2026 · Artificial Intelligence

ChartNet: MIT/IBM’s Million‑Scale Synthetic Chart Dataset with 1.5M Diverse Samples

MIT and IBM researchers introduce ChartNet, the largest code‑guided synthetic chart dataset containing 1.5 million multimodal samples across 24 chart types and six libraries, and demonstrate that fine‑tuning visual‑language models on it yields consistent, significant gains on chart reconstruction, data extraction, summarization, and reasoning tasks, outperforming much larger off‑the‑shelf models including GPT‑4o.

AI researchChartNetMultimodal Dataset
0 likes · 13 min read
ChartNet: MIT/IBM’s Million‑Scale Synthetic Chart Dataset with 1.5M Diverse Samples
Machine Heart
Machine Heart
Jun 7, 2026 · Artificial Intelligence

FusionRoute: Token-Level Expert Routing and Self-Correction for Multi-LLM Collaboration

FusionRoute introduces a token‑level routing framework that dynamically selects the most suitable expert LLM for each token and adds a complementary generation step, enabling fine‑grained, stable multi‑model collaboration that outperforms existing sequence‑level and expert‑selection methods across diverse benchmarks.

AI researchModel MergingSelf-Correction
0 likes · 11 min read
FusionRoute: Token-Level Expert Routing and Self-Correction for Multi-LLM Collaboration
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Jun 4, 2026 · Artificial Intelligence

Is This the Last Human-Written Paper? Converting PDFs into AI-Executable Research Artifacts

A collaborative paper by 37 scholars from Stanford, MIT, CMU and others argues that the centuries‑old PDF format imposes hidden storytelling and engineering taxes, proposes a four‑layer Agent‑Native Research Artifact (ARA) to preserve full experimental detail, and shows through benchmarks that ARA dramatically improves AI agents' understanding, reproduction and extension of research.

AI researchAgent-native artifactsScientific Publishing
0 likes · 10 min read
Is This the Last Human-Written Paper? Converting PDFs into AI-Executable Research Artifacts
SuanNi
SuanNi
Jun 2, 2026 · Artificial Intelligence

Harvard’s AutoScientists Lets AI Agents Self‑Organize Research Teams and Outperform Traditional AI Agents

AutoScientists, a Harvard‑built system where nine AI agents self‑organize via a shared state without a central commander, achieves a 74.4% average rank on BioML‑Bench, runs GPT training experiments 1.9× faster, and improves ProteinGym fitness prediction by 12.5%, while ablation studies reveal the critical role of each of its four core mechanisms.

AI agentsAI researchAutoScientists
0 likes · 12 min read
Harvard’s AutoScientists Lets AI Agents Self‑Organize Research Teams and Outperform Traditional AI Agents
Machine Heart
Machine Heart
Jun 2, 2026 · Artificial Intelligence

When AI Becomes Its Own Data Engineer: Inside DataMaster

DataMaster introduces an autonomous AI data engineer that automatically searches, cleans, combines, and reuses data, enabling fixed models and training pipelines to achieve substantial performance gains across benchmarks such as MLE‑Bench Lite and PostTrainBench, including a 31.0% GPQA score.

AI researchData-Centric AIDataMaster
0 likes · 11 min read
When AI Becomes Its Own Data Engineer: Inside DataMaster
PaperAgent
PaperAgent
May 30, 2026 · Artificial Intelligence

DeepSeek Researcher Co‑authors Two New Papers on Autonomous AI Research and Continual Learning

The article summarizes two recent DeepSeek papers—one presenting an L1–L5 taxonomy and four architecture patterns for autonomous research agents, the other proposing a three‑dimensional taxonomy for continual learning, detailing method families, a self‑improvement phase diagram, experimental comparisons, an impossibility theorem, and the production statistics of the Deli AutoResearch framework.

AI researchBenchmark analysisContinual Learning
0 likes · 12 min read
DeepSeek Researcher Co‑authors Two New Papers on Autonomous AI Research and Continual Learning
Machine Heart
Machine Heart
May 25, 2026 · Artificial Intelligence

How DeepMind’s AI Solved Nine Erdős Problems for Only a Few Hundred Dollars Each

DeepMind’s AlphaProof Nexus framework enabled an AI agent to automatically prove and verify nine long‑standing Erdős conjectures at a cost of only a few hundred dollars per problem, using a simple “think‑try” loop and a more advanced multi‑agent evolution architecture, and demonstrating a shift toward leveraging raw large‑model reasoning for formal mathematics.

AI researchAlphaProof NexusDeepMind
0 likes · 11 min read
How DeepMind’s AI Solved Nine Erdős Problems for Only a Few Hundred Dollars Each
SuanNi
SuanNi
May 22, 2026 · Artificial Intelligence

All‑In‑One Image & Video: ByteDance’s Deployable Native Multimodal Model Lance

Lance, ByteDance’s newly open‑sourced 3‑billion‑parameter multimodal model, runs on a single 40 GB GPU, tops HuggingFace trend charts, and achieves leading scores on DPG Bench, GenEval, and video generation benchmarks while surpassing several state‑of‑the‑art single‑modal models.

AI researchByteDanceLance
0 likes · 3 min read
All‑In‑One Image & Video: ByteDance’s Deployable Native Multimodal Model Lance
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
May 21, 2026 · Artificial Intelligence

Visual Generation Meets Slow Thinking: Decoding New Multimodal Reasoning Paradigms from CVPR 2026

This article curates ten standout CVPR 2026 papers that introduce novel multimodal interaction frameworks, active video avatars, unified image customization, artistic poster generation, information‑theoretic video compression, all‑purpose visual reasoning models, 3D‑grounded spatial reasoning, interleaved text‑visual generation, and unified fine‑grained video understanding, each achieving state‑of‑the‑art performance.

AI researchCVPRMultimodal
0 likes · 13 min read
Visual Generation Meets Slow Thinking: Decoding New Multimodal Reasoning Paradigms from CVPR 2026
Machine Heart
Machine Heart
May 20, 2026 · Industry Insights

ByteDance Scholarship Goes Global: Tracking the Careers of 67 Winners Over Five Years

The 2026 ByteDance Scholarship opens to worldwide applicants, expands slots and funding, and now accepts stage‑level results; a five‑year review shows 67 awardees—spanning PhDs, masters and undergraduates—from top universities who have entered top AI labs, founded startups, or taken faculty positions, illustrating how early‑stage research often precedes industry trends.

AI researchAI scholarshipByteDance
0 likes · 12 min read
ByteDance Scholarship Goes Global: Tracking the Careers of 67 Winners Over Five Years
Kuaishou Tech
Kuaishou Tech
May 18, 2026 · Artificial Intelligence

How ALM‑MTA Improves Multi‑Touch Attribution with Front‑Door Identification and Adversarial Modeling

The ALM‑MTA method combines front‑door causal adjustment with an adversarial proxy for the unobserved mediator, eliminating hidden confounding in multi‑touch attribution and delivering more reliable uplift estimates that boosted Kuaishou's DAU by 0.6% and AUC by 11% over SOTA baselines, as reported in an ICLR 2026 paper.

AI researchadversarial learningcausal attribution
0 likes · 13 min read
How ALM‑MTA Improves Multi‑Touch Attribution with Front‑Door Identification and Adversarial Modeling
Machine Heart
Machine Heart
May 17, 2026 · Artificial Intelligence

What Exactly Is a World Model? History, Technology, and the $10 B Bet

The article traces the two decades‑long, parallel research lines that birthed video world models—dreaming agents in reinforcement learning and learning physics from human video—explains how they converged in 2024‑2025, evaluates current capabilities and limitations, and analyzes the $10 billion investment landscape and strategic moves by NVIDIA, OpenAI, and others.

AI researchReinforcement LearningSimulation
0 likes · 32 min read
What Exactly Is a World Model? History, Technology, and the $10 B Bet
Data Party THU
Data Party THU
Apr 30, 2026 · Artificial Intelligence

Turning Transformers into Mamba: How Apple Linearized Inference Costs

Apple introduced a two‑step cross‑architecture distillation method that converts costly quadratic‑time Transformers into cheaper linear‑time Mamba models, preserving most of the original performance while dramatically reducing inference cost.

AI researchLinear AttentionMamba
0 likes · 8 min read
Turning Transformers into Mamba: How Apple Linearized Inference Costs
Machine Heart
Machine Heart
Apr 30, 2026 · Artificial Intelligence

Can a Pre‑1930 Language Model Infer Einstein’s Relativity? Insights from the Talkie‑1930 Project

Researchers built a 13‑billion‑parameter model trained only on texts published before 1931, called Talkie‑1930, and used surprise‑based metrics, programming tests, and a modern‑twin comparison to explore how far such a historically‑constrained model can extrapolate future knowledge and reveal data‑leakage challenges.

AI researchHumanEvaldata leakage
0 likes · 10 min read
Can a Pre‑1930 Language Model Infer Einstein’s Relativity? Insights from the Talkie‑1930 Project
PaperAgent
PaperAgent
Apr 30, 2026 · Artificial Intelligence

How Agentic AI is Redefining World Modeling

The article reviews the paper "Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond", introducing a two‑axis framework (capability levels L1‑L3 and law domains) to map diverse world‑modeling systems, highlighting that most current systems stall at L1, that explicit law encoding is crucial for long‑term stability, and that L3 represents the ultimate, self‑evolving model.

AI agentsAI researchAgentic AI
1 likes · 6 min read
How Agentic AI is Redefining World Modeling
Data Party THU
Data Party THU
Apr 29, 2026 · Artificial Intelligence

How Far Can Unsupervised RL for Large Models Go? A Systematic Answer from a Tsinghua Team

The article analyzes the scaling limits of unsupervised reinforcement learning for large language models, revealing that intrinsic‑reward methods initially boost performance but inevitably collapse, proposes a unified theory and a model‑collapse metric to predict trainability, and argues that external‑reward approaches are the scalable path forward.

AI researchRL scalingexternal rewards
0 likes · 11 min read
How Far Can Unsupervised RL for Large Models Go? A Systematic Answer from a Tsinghua Team
Machine Heart
Machine Heart
Apr 25, 2026 · Artificial Intelligence

Can Multi-Model Co-Evolution Shatter the Single-Model Ceiling? Squeeze Evolve Achieves Validator-Free SOTA Inference

The paper introduces Squeeze Evolve, a validator‑free multi‑model evolutionary framework that orchestrates diverse large language models to break the performance ceiling of any single model, delivering up to 23‑point accuracy improvements and 1.4‑3.3× cost reductions across math, vision, and scientific benchmarks.

AI researchSqueeze EvolveTest-Time Scaling
0 likes · 8 min read
Can Multi-Model Co-Evolution Shatter the Single-Model Ceiling? Squeeze Evolve Achieves Validator-Free SOTA Inference
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Apr 23, 2026 · Industry Insights

Should You Take a Tencent AI Internship? Key Factors to Consider

The article examines whether a Tencent AI internship is worth pursuing by analyzing the program’s growth stage, unique user ecosystem, mentorship structure, compensation model, and early‑year advantages, illustrated with real intern case studies, to help students decide what they aim to gain from the experience.

AI internshipAI researchTencent
0 likes · 14 min read
Should You Take a Tencent AI Internship? Key Factors to Consider
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Apr 16, 2026 · Artificial Intelligence

Evidence Mining for Explainable AI: Methods and Applications

The talk introduces evidence‑mining techniques that extract supporting information from input text to improve model explainability, discusses the shortcut‑learning pitfalls of existing methods, and presents a new approach that enhances reliability and integrates with large‑model chain‑of‑thought compression for more interpretable, efficient reasoning.

AI researchevidence miningexplainable AI
0 likes · 4 min read
Evidence Mining for Explainable AI: Methods and Applications
Meituan Technology Team
Meituan Technology Team
Apr 16, 2026 · Artificial Intelligence

Can End-to-End Diffusion TTS Beat Traditional Pipelines? Inside LongCat-AudioDiT

LongCat-AudioDiT introduces a wave‑VAE plus diffusion Transformer architecture that eliminates intermediate spectrograms, solves training‑inference mismatch with dual constraints, replaces classifier‑free guidance with adaptive projection guidance, and achieves state‑of‑the‑art zero‑shot voice cloning performance on multiple benchmarks.

AI researchText-to-Speechaudio generation
0 likes · 12 min read
Can End-to-End Diffusion TTS Beat Traditional Pipelines? Inside LongCat-AudioDiT
Data STUDIO
Data STUDIO
Apr 14, 2026 · Artificial Intelligence

Can ChatGPT Deep Research Double Your Research Efficiency?

The article explains how ChatGPT Deep Research transforms ordinary web searches into full‑fledged research reports, compares three leading Deep Research tools, outlines nine practical use cases, warns of common pitfalls, and offers prompt‑engineering tips for both individual and enterprise adoption.

AI researchChatGPTDeep Research
0 likes · 16 min read
Can ChatGPT Deep Research Double Your Research Efficiency?
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Apr 9, 2026 · Artificial Intelligence

Google DeepMind’s Deep Think Dominates Eight Language Olympiads and Solves Four AI Challenges

Google DeepMind’s Deep Think model posted top‑tier scores in eight language‑specific Olympiads—from IMO gold to ICPC finals—while also tackling open scientific problems, yet the results rely on internal evaluations without third‑party verification, highlighting both a breakthrough in multilingual AI reasoning and the need for transparent benchmarking.

AI benchmarkingAI researchDeep Think
0 likes · 9 min read
Google DeepMind’s Deep Think Dominates Eight Language Olympiads and Solves Four AI Challenges
PaperAgent
PaperAgent
Apr 6, 2026 · Artificial Intelligence

Can LLMs Self‑Improve After Deployment? Inside Microsoft’s Online Experiential Learning

Microsoft’s Online Experiential Learning framework lets large language models continuously self‑evolve after deployment by extracting experience from user interactions and consolidating it into model parameters, eliminating the need for human labels, reward models, or server‑side environment access, and demonstrating scalable gains across tasks and model sizes.

AI researchLLMcontinuous training
0 likes · 9 min read
Can LLMs Self‑Improve After Deployment? Inside Microsoft’s Online Experiential Learning
Data Party THU
Data Party THU
Apr 5, 2026 · Artificial Intelligence

How to Beat Shortcut Learning for Better OOD Generalization in Vision Models

Visual and vision-language models excel under IID benchmarks but often fail on out-of-distribution data due to shortcut learning; this article examines the problem, explains its causes, and proposes data-level and model-level interventions—including StillMix, FLASH, and SPARCL—to improve OOD robustness.

AI researchData AugmentationModel Design
0 likes · 7 min read
How to Beat Shortcut Learning for Better OOD Generalization in Vision Models
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Apr 2, 2026 · Artificial Intelligence

How Large Language Models Can Self‑Improve: A Technical Review and Future Outlook

This article surveys the emerging self‑improvement paradigm for large language models, presenting a closed‑loop lifecycle comprising data acquisition, selection, model optimization, inference refinement, and an autonomous evaluation layer, and discusses current limitations and research directions toward fully autonomous LLM evolution.

AI researchAutonomous EvaluationLLM
0 likes · 11 min read
How Large Language Models Can Self‑Improve: A Technical Review and Future Outlook
PaperAgent
PaperAgent
Mar 28, 2026 · Artificial Intelligence

How ACCORD Breaks Concept Coupling in Custom Text‑to‑Image Generation

The ACCORD framework formalizes the concept‑coupling issue in text‑to‑image diffusion models as a statistical dependency problem and resolves it with two plug‑and‑play regularization losses, dramatically improving fidelity and text control without altering model architecture.

ACCORDAI researchconcept coupling
0 likes · 7 min read
How ACCORD Breaks Concept Coupling in Custom Text‑to‑Image Generation
SuanNi
SuanNi
Mar 25, 2026 · Artificial Intelligence

How LeWorldModel Learns Physics from Pixels in Hours – A Deep Dive

LeWorldModel (LeWM) is a compact AI world model that learns real‑world physics directly from raw pixel streams using only two simple mathematical rules, achieving dramatically faster planning and robust physical intuition compared to prior large‑scale models.

AI researchModel Predictive Controlphysics learning
0 likes · 14 min read
How LeWorldModel Learns Physics from Pixels in Hours – A Deep Dive
AI Architecture Hub
AI Architecture Hub
Mar 25, 2026 · Artificial Intelligence

How Memento-Skills Enables Continuous Learning for Frozen LLM Agents

The article analyzes the limitations of frozen LLM agents—fixed parameters, loss of state, and costly fine‑tuning—and introduces the Memento‑Skills framework, which adds an external, evolvable skill memory to achieve deployment‑time learning, detailed architecture, optimization knobs, and strong experimental gains.

AI researchContinuous LearningDeployment-Time Learning
0 likes · 14 min read
How Memento-Skills Enables Continuous Learning for Frozen LLM Agents
AIWalker
AIWalker
Mar 20, 2026 · Artificial Intelligence

Plug‑and‑Play reAR Boosts Visual AR to SOTA Quality with Only 177M Parameters

The paper introduces reAR, a plug‑and‑play regularization framework that aligns generator and tokenizer representations in visual autoregressive models, dramatically improving image quality and matching large diffusion models while using far fewer parameters, and validates the approach with extensive experiments, ablations, and scalability analysis.

AI researchRegularizationimage generation
0 likes · 20 min read
Plug‑and‑Play reAR Boosts Visual AR to SOTA Quality with Only 177M Parameters
AIWalker
AIWalker
Mar 17, 2026 · Artificial Intelligence

How a 4B-Parameter Open-Source Model Outperforms 14B Multimodal Giants

InternVL-U, a 4‑billion‑parameter unified multimodal model released as open source, combines a 2B MLLM backbone with a 1.7B visual generation head and, through a reasoning‑centric data pipeline and Chain‑of‑Thought guidance, achieves superior understanding, generation, and editing performance that surpasses much larger 14‑20B models on multiple benchmarks.

AI researchInternVL-UMultimodal
0 likes · 22 min read
How a 4B-Parameter Open-Source Model Outperforms 14B Multimodal Giants
AI Architecture Path
AI Architecture Path
Mar 17, 2026 · Artificial Intelligence

Automating LLM Tuning with Autoresearch: AI Agents on a Single GPU

Autoresearch, an open‑source project by Andrej Karpathy, enables AI agents to autonomously modify code, run experiments, and evaluate results for LLM tuning on a single GPU, dramatically reducing manual hyper‑parameter work, standardizing experiments, and offering low‑cost, reproducible research with clear limitations and practical setup steps.

AI researchLLM tuningautonomous agents
0 likes · 11 min read
Automating LLM Tuning with Autoresearch: AI Agents on a Single GPU
AI Explorer
AI Explorer
Mar 15, 2026 · Artificial Intelligence

Large Models May Break Language Training Dependence, Redefining Intelligence

A new study suggests that large AI models could reduce their reliance on massive text corpora by early‑fusing multimodal data such as video and sensor streams, potentially slashing training costs, improving generalization, and prompting a shift toward more embodied notions of intelligence.

AI researchMultimodal Learningembodied intelligence
0 likes · 6 min read
Large Models May Break Language Training Dependence, Redefining Intelligence
AI Engineering
AI Engineering
Mar 10, 2026 · Artificial Intelligence

Yann LeCun’s New AMI Labs Secures $1.03B to Build a World‑Model Alternative to LLMs

Yann LeCun and Alexandre LeBrun have launched AMI Labs, raising $1.03 billion in Europe’s largest seed round to develop JEPA—a world‑model architecture intended to replace LLMs for high‑risk domains, with all code and papers open‑sourced, a 5‑10‑year horizon, and backing from NVIDIA, Samsung, Bezos’ venture, and others.

AI researchAMI LabsJEPA
0 likes · 3 min read
Yann LeCun’s New AMI Labs Secures $1.03B to Build a World‑Model Alternative to LLMs
AIWalker
AIWalker
Mar 8, 2026 · Artificial Intelligence

How VisionPangu’s 1.7B Model Beats Larger LLMs in Detailed Image Captioning

VisionPangu demonstrates that a compact 1.7 B‑parameter multimodal model can generate richly detailed, coherent image descriptions that rival much larger models by leveraging high‑quality dense data, a three‑part architecture, and a two‑stage deep alignment training strategy.

AI researchData QualityImage Captioning
0 likes · 13 min read
How VisionPangu’s 1.7B Model Beats Larger LLMs in Detailed Image Captioning
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Mar 6, 2026 · Artificial Intelligence

15‑Person Overseas Chinese Team Builds Uni‑1, a Unified Image Model Surpassing Nano Banana

The article reviews Uni‑1, a decoder‑only transformer that unifies visual understanding and generation, details its architecture, benchmark superiority on RISEBench and ODinW‑13, showcases diverse visual examples where it outperforms GPT Image 1.5 and Nano Banana Pro, and highlights the small elite team behind the breakthrough.

AI researchLuma AIRISEBench
0 likes · 14 min read
15‑Person Overseas Chinese Team Builds Uni‑1, a Unified Image Model Surpassing Nano Banana
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Mar 5, 2026 · Artificial Intelligence

Can AI Self‑Improve? Inside a Stanford PhD Defense on Continually Self‑Improving AI

Zitong Yang’s Stanford PhD defense introduced “continually self‑improving AI,” a system that autonomously refines its own parameters, generates synthetic training data, and even designs its own learning algorithms, with experiments on synthetic continual training, synthetic‑bootstrap pre‑training, and AI‑design‑AI demonstrating measurable gains over static baselines.

AI researchContinual LearningPretraining
0 likes · 35 min read
Can AI Self‑Improve? Inside a Stanford PhD Defense on Continually Self‑Improving AI
AI Frontier Lectures
AI Frontier Lectures
Feb 28, 2026 · Artificial Intelligence

Can Reinforcement Learning Revolutionize Text-to-3D Generation? A Deep Dive

This article presents a systematic investigation of applying reinforcement learning to text‑to‑3D generation, detailing reward design, algorithm selection, a new 3D benchmark, a hierarchical GRPO framework, extensive ablations, and the resulting performance gains and limitations.

AI researchGenerative ModelsReinforcement Learning
0 likes · 13 min read
Can Reinforcement Learning Revolutionize Text-to-3D Generation? A Deep Dive
PaperAgent
PaperAgent
Feb 25, 2026 · Artificial Intelligence

How Contextual Co-Player Inference Enables Robust Multi-Agent Cooperation

These two recent Google papers advance multi‑agent reinforcement learning: one introduces contextual co‑player inference to achieve robust cooperation without explicit meta‑learning, while the other presents AlphaEvolve, a large‑language‑model‑driven evolutionary framework that automatically discovers novel MARL algorithms such as VAD‑CFR and SHOR‑PSRO.

AI researchCFRLLM-driven algorithm discovery
0 likes · 13 min read
How Contextual Co-Player Inference Enables Robust Multi-Agent Cooperation
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Feb 14, 2026 · Artificial Intelligence

Latent Forcing: Reordering Diffusion Steps Boosts Pixel‑Level Image Quality

The new Latent Forcing technique from Fei‑Fei Li’s team reorders the diffusion trajectory, first generating a latent structural sketch and then refining pixel details, which restores efficiency of latent‑space models while preserving 100 % pixel fidelity, achieving state‑of‑the‑art FID scores on ImageNet‑256.

AI researchImageNetdiffusion models
0 likes · 6 min read
Latent Forcing: Reordering Diffusion Steps Boosts Pixel‑Level Image Quality
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Feb 10, 2026 · Artificial Intelligence

LeCun Team’s Triple Breakthrough: Sparse Representations, Gradient Planning, and Lightweight JEPA for World Models

LeCun’s three new papers—Rectified LpJEPA, GRASP, and EB‑JEPA—address dense feature bottlenecks, inefficient gradient‑free planning, and heavyweight codebases by introducing sparsity‑preserving regularization, a parallel gradient‑based planner, and a lightweight modular library, delivering high‑performance world‑model representations that run on a single GPU.

AI researchJEPASelf-Supervised Learning
0 likes · 11 min read
LeCun Team’s Triple Breakthrough: Sparse Representations, Gradient Planning, and Lightweight JEPA for World Models
JD Cloud Developers
JD Cloud Developers
Feb 4, 2026 · Artificial Intelligence

How Deep Research Transforms LLMs into Autonomous AI Researchers

This article examines Deep Research, an AI system that adds autonomous planning and deep reasoning to large language models, enabling them to browse the web, perform long‑chain reasoning, and generate professional, citation‑rich reports for complex tasks such as industry trend analysis and technical competitive research.

AI researchInformation RetrievalLLM
0 likes · 22 min read
How Deep Research Transforms LLMs into Autonomous AI Researchers
JD Tech Talk
JD Tech Talk
Feb 4, 2026 · Artificial Intelligence

How Deep Research Turns LLMs into Autonomous AI Researchers

This article explains the background, core features, underlying ReAct‑based architecture, and engineering solutions of Deep Research—a system that equips large language models with autonomous planning, long‑chain reasoning, and professional report generation to tackle complex information‑intensive tasks.

AI researchInformation RetrievalLLM
0 likes · 21 min read
How Deep Research Turns LLMs into Autonomous AI Researchers
PaperAgent
PaperAgent
Feb 2, 2026 · Artificial Intelligence

How Kimi K2.5 Achieves Multimodal Mastery with Joint Training and Agent Swarms

The Kimi K2.5 technical report reveals how a Chinese team combined joint text‑vision training, a novel Zero‑Vision SFT method, and a parallel agent‑swarm architecture to deliver top‑ranked multimodal performance, dramatically faster inference, and open‑source access for broader AI research.

AI researchAgent SwarmKimi K2.5
0 likes · 9 min read
How Kimi K2.5 Achieves Multimodal Mastery with Joint Training and Agent Swarms
Data Party THU
Data Party THU
Jan 31, 2026 · Artificial Intelligence

Can LLMs Learn While Being Tested? Inside the TTT-Discover Breakthrough

The article examines the Test‑Time Training to Discover (TTT‑Discover) approach, which applies reinforcement learning during inference to let large language models continuously improve on single test problems, and reports strong results across mathematics, GPU kernel optimization, algorithm design, and biology.

AI researchLLMReinforcement Learning
0 likes · 9 min read
Can LLMs Learn While Being Tested? Inside the TTT-Discover Breakthrough
AI Frontier Lectures
AI Frontier Lectures
Jan 30, 2026 · Artificial Intelligence

How SplatSSC Revolutionizes Semantic Scene Completion with Depth‑Guided Gaussian Splatting

SplatSSC introduces a depth‑guided Gaussian splatting framework that replaces random primitive initialization with geometry‑aware priors and a decoupled aggregation module, achieving state‑of‑the‑art performance on indoor semantic scene completion while dramatically reducing computational overhead and eliminating floaters.

3D PerceptionAI researchGaussian Splatting
0 likes · 10 min read
How SplatSSC Revolutionizes Semantic Scene Completion with Depth‑Guided Gaussian Splatting
Baobao Algorithm Notes
Baobao Algorithm Notes
Jan 26, 2026 · Artificial Intelligence

From Search Ads to Foundation Models: My Journey Building the EvoCUA GUI Agent

The author explains why he transitioned from search advertising algorithms to foundation model research, outlines the four typical activities of base‑model teams, and shares detailed technical insights, experimental practices, and scaling strategies that led the EvoCUA GUI Agent to achieve open‑source SOTA on OSWorld.

AI researchFoundation ModelsGUI agents
0 likes · 17 min read
From Search Ads to Foundation Models: My Journey Building the EvoCUA GUI Agent
PaperAgent
PaperAgent
Jan 25, 2026 · Artificial Intelligence

How Deep GraphRAG Solves Retrieval’s Three‑Way Dilemma with Hierarchical Search

Deep GraphRAG tackles the three‑fold dilemma of traditional Retrieval‑Augmented Generation by introducing hierarchical global‑to‑local retrieval, a beam‑search dynamic reordering that cuts latency, and a DW‑GRPO reinforcement‑learning module that adaptively weights rewards, achieving near‑state‑of‑the‑art performance with up to 86% faster inference.

AI researchGraphRAGHierarchical Retrieval
0 likes · 5 min read
How Deep GraphRAG Solves Retrieval’s Three‑Way Dilemma with Hierarchical Search
PaperAgent
PaperAgent
Jan 25, 2026 · Industry Insights

Top 10 Chinese Large Models to Watch: Features, Benchmarks, and Download Links

This roundup highlights ten cutting‑edge Chinese AI models—including Qwen3‑TTS, LongCat‑Flash‑Thinking‑2601, GLM‑4.7‑Flash, STEP3‑VL‑10B, Baichuan‑M3, and Youtu‑LLM—detailing their multilingual capabilities, architecture innovations, performance claims, and providing direct repository links for researchers and developers.

AI researchChinese AIMultimodal
0 likes · 7 min read
Top 10 Chinese Large Models to Watch: Features, Benchmarks, and Download Links
PaperAgent
PaperAgent
Jan 20, 2026 · Artificial Intelligence

How Intrinsic Self‑Critique Boosts LLM Planning Accuracy to 89% %​

Google DeepMind's new "Intrinsic Self‑Critique" method lets large language models iteratively self‑evaluate and rewrite their plans, raising Blocksworld planning accuracy from 49.8% to 89.3% and setting new records across multiple planning benchmarks.

AI researchLLMintrinsic evaluation
0 likes · 5 min read
How Intrinsic Self‑Critique Boosts LLM Planning Accuracy to 89% %​
BirdNest Tech Talk
BirdNest Tech Talk
Jan 11, 2026 · Artificial Intelligence

How AI Agents Overcome Context Window Limits: Gemini vs Manus Deep Research

The article analyzes the context‑window bottleneck of large language models, compares two architectural strategies—strengthening the model (Gemini Deep Research) and parallel agent decomposition (Manus Wide Research)—and details a wind‑power investment case study, technical implementation, and future directions.

AI researchAgent ArchitectureReAct
0 likes · 16 min read
How AI Agents Overcome Context Window Limits: Gemini vs Manus Deep Research
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Jan 11, 2026 · Artificial Intelligence

Insights from NeurIPS 2025: Modeling Distributions and Venturing Beyond Them

The report summarizes NeurIPS 2025 in San Diego, highlighting four NIRC papers on noise‑robust 3D human pose estimation, LVLM video‑anomaly understanding, and hand‑object reconstruction, and discusses broader industry trends such as feed‑forward generation and large‑scale pre‑training showcased by leading AI companies.

3D human pose estimationAI researchLVLM
0 likes · 5 min read
Insights from NeurIPS 2025: Modeling Distributions and Venturing Beyond Them
Data Party THU
Data Party THU
Jan 7, 2026 · Artificial Intelligence

Why the Common KL Penalty in LLM RL Training Is Biased—and How to Fix It

A recent study reveals that the widely used KL regularization in LLM reinforcement learning (RLVR) is mathematically biased, leading to unstable training and poorer generalization, and shows that moving the KL term back to the reward with a simple K1 estimator can boost out‑of‑domain performance by up to 20%.

AI researchKL regularizationLLM training
0 likes · 10 min read
Why the Common KL Penalty in LLM RL Training Is Biased—and How to Fix It
PaperAgent
PaperAgent
Jan 6, 2026 · Artificial Intelligence

How Recursive Language Models Enable Unlimited Context for LLMs

Recursive Language Models (RLM) offer a cost‑effective alternative to expanding LLM context windows by storing prompts as variables and enabling recursive calls, allowing models to process over 100,000 tokens, with experiments showing superior performance and lower median costs compared to baseline approaches.

AI researchLLM scalingRecursive Language Models
0 likes · 5 min read
How Recursive Language Models Enable Unlimited Context for LLMs
HyperAI Super Neural
HyperAI Super Neural
Jan 5, 2026 · Artificial Intelligence

WorldPlay: Real‑Time Interactive World Modeling with Long‑Term Geometry Consistency

Tencent’s HyperAI team introduces WorldPlay, an open‑source real‑time interactive world model that achieves 24 FPS 720p video generation while preserving long‑term geometric consistency through dual‑action representation, dynamic context memory reconstruction, and a novel context‑forcing distillation, and also showcases Maya1 emotional TTS and RFdiffusion3 protein design models.

AI researchWorldPlaycontext memory
0 likes · 6 min read
WorldPlay: Real‑Time Interactive World Modeling with Long‑Term Geometry Consistency
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Dec 30, 2025 · Artificial Intelligence

Bridging Tokenizer Gaps: Cross-Tokenizer Knowledge Distillation at AAAI 2026

This paper introduces SeDi, a semantics‑ and distribution‑aware cross‑tokenizer knowledge distillation framework that aligns teacher and student token spaces via bipartite graph components and top‑K re‑encoding, achieving state‑of‑the‑art performance and lower exposure bias on multiple LLM benchmarks.

AI researchSemantic Alignmentcross-tokenizer distillation
0 likes · 10 min read
Bridging Tokenizer Gaps: Cross-Tokenizer Knowledge Distillation at AAAI 2026
PaperAgent
PaperAgent
Dec 29, 2025 · Artificial Intelligence

Unveiling Bottom‑up Policy Optimization: Boosting LLM Reasoning with Internal Strategies

This article introduces Bottom‑up Policy Optimization (BuPO), a novel reinforcement‑learning framework that treats large language models as collections of internal layer and modular policies, revealing distinct inference entropy patterns in Llama and Qwen‑3 and demonstrating superior performance on complex mathematical reasoning benchmarks.

AI researchBottom-up OptimizationInternal Policy
0 likes · 10 min read
Unveiling Bottom‑up Policy Optimization: Boosting LLM Reasoning with Internal Strategies
PaperAgent
PaperAgent
Dec 26, 2025 · Artificial Intelligence

What Google’s 2025 AI Breakthroughs Reveal About the Future of Intelligent Agents

Google’s 2025 research recap highlights eight major breakthroughs—from the Gemini 3 series achieving unprecedented multimodal reasoning and efficiency, to AI‑driven advances in scientific discovery, creative generation, quantum computing, climate resilience, and responsible AI safety—showcasing how intelligent agents are reshaping products, research, and global challenges.

AI researchAI safetyclimate AI
0 likes · 10 min read
What Google’s 2025 AI Breakthroughs Reveal About the Future of Intelligent Agents