Tagged articles

Cross-Model Transfer

3 articles · Page 1 of 1

Sep 24, 2026 · Artificial Intelligence

ModularRSI: Agents Self-Improve by Evolving Harness, Not Model Weights

ModularRSI demonstrates that AI agents can continuously self-improve by evolving their harness—the surrounding system mechanisms like tool use, context management, and task completion detection—while keeping the base model frozen, achieving performance gains on Terminal-Bench and SWE-Bench that transfer across domains and models.

Agent Self-ImprovementCross-Domain TransferCross-Model Transfer

0 likes · 18 min read

ModularRSI: Agents Self-Improve by Evolving Harness, Not Model Weights

Data Party THU

Jun 11, 2026 · Artificial Intelligence

Boost 18 LLM Agents Without Retraining Using LIFE‑HARNESS

The article introduces LIFE‑HARNESS, a runtime‑interface adaptation framework that keeps model weights unchanged, extracts reusable failure patterns from a single model's training trace, and achieves an average 88.5% relative performance gain across 18 LLM agents and 7 deterministic environments, with successful transfer to 17 other models.

Cross-Model TransferLLM agentsRuntime Harness

0 likes · 8 min read

Boost 18 LLM Agents Without Retraining Using LIFE‑HARNESS

SuanNi

May 27, 2026 · Artificial Intelligence

Can Agent Skills Be Trained Like Neural Networks? SkillOpt Demonstrates Success

SkillOpt treats an agent’s Skill document as a trainable external state, applying classic deep‑learning tools such as epochs, batch size, learning rate and validation gating, and in experiments across 52 benchmark units it lifts GPT‑5.5 performance by an average of 23.5 points while enabling cross‑model and cross‑environment transfer with no additional inference cost.

Agent SkillCross-Model TransferDeep Learning Optimization

0 likes · 11 min read

Can Agent Skills Be Trained Like Neural Networks? SkillOpt Demonstrates Success