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Every published item, in order · 9845 items
Oct 21
PapersBuildersSelected21:29
Reasoning Models Show Generalization on Rare Text Encodings

The shift from memorization-by-prevalence to genuine generalization changes how practitioners should reason about edge-case reliability in deployed models. Builders evaluating models on adversarial or low-frequency inputs can expect reasoning models to outperform earlier LLMs where no direct alternative comparison exists.

Riley Goodside@goodsideSource
PapersBuildersSelected13:22
Meta ProPerAgent Harness Lifts Proactive Agent Accuracy to 38%

The core insight is that timing, not memory volume, is the bottleneck for proactive assistants: a dedicated filtering call before the model sees a memory does most of the work. Builders of always-on or wearable agents can apply this retrieve-then-filter pattern without fine-tuning, and RL on top adds a further gain. It is a useful comparison point against standard RAG pipelines that flood the model with context.

DAIR.AI@dair_aiSource
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PapersBuildersMulti-sourceSelected10:17
Microsoft Paper Co-Evolves RL Harness and Qwen3.5 Model

Co-evolving the harness and the model appears to unlock gains that optimizing either alone cannot reach, suggesting the harness itself is a first-class lever in RL training. Teams training coding agents can copy the loop to squeeze more performance from mid-size open models like Qwen3.5-35B-A3B. The distillation trick of using Opus 4.8 as the harness proposer also offers a cheaper route to borrow frontier-model judgment.

Event
DAIR.AI@dair_aiSource
PapersBuildersMulti-sourceSelected74°10:11
Agent S4 Evolves Its Own Computer-Use Interfaces

Interface design itself becomes a learnable, evolving capability, meaning smaller open models can beat frontier systems on computer-use tasks at a fraction of the cost. Builders working on agents can reuse the open-source repository and apply the evolution loop to their own workflows. The results set a concrete cost-performance benchmark against GPT-5.5 and Claude Opus 4.8 that teams choosing an agent stack can weigh directly.

Event
arXiv cs.AI@Saaket Agashe et al. (8)Source
PapersBuildersSelected10:11
R2S Fixes Myopic Teacher Scoring in Agentic On-Policy Distillation

The non-obvious insight is that the failure of agentic distillation comes from the scorer's myopia, not from students drifting out of the teacher's reliable region, so the fix targets the teacher instead of constraining rollouts. Builders training agents via distillation can adopt R2S without task reward labels, using consensus or an LLM judge as the check. Code and details are on the project page at tree-shu-zhao.github.io/r2s/.

arXiv cs.AI@Shu Zhao, Tan YuSource

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