arXiv AI
SLDBench v2: Active Scaling Law Discovery
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arXiv AI
Aug 17
AI Research Preference Models
arXiv:2608. 13940v1 Announce Type: new Abstract: AI research agents (AIRA) can now propose, implement, and evaluate their own machine learning experiments, but progress on frontier tasks is throttled by cost: a candidate solution can be written in minutes, whereas evaluating it can take hours to days of GPU time.
arXiv AI
Oct 7
Stateless Language Agents: Scaling Long-Horizon Automated Research
arXiv:2610.07625v1 Announce Type: cross Abstract: Automated research systems increasingly run LLM agents over long horizons, but more inference does not by itself produce more progress: agents replay...
arXiv AI
Oct 2
Sharpening Tax in Post-Training
The paper investigates how reinforcement learning post‑training of large language models (LLMs) tends to sharpen existing behaviors, improving single‑shot accuracy but reducing solution coverage. It shows that pre‑trained LLMs, when paired with a lightweight inference harness, can outperform post‑trained models in coverage for agentic tasks that require multi‑turn tool use. The authors introduce the Sharpening Tax metric to quantify this trade‑off, analyze its prevalence across 14 model pairs and 42 benchmark cases, and propose a Bayesian sampler, PTGS, that mitigates the tax by adapting sampling temperature to prompt difficulty.
arXiv Machine Learning
Jun 18
GrowthHacker: Automated Off-Policy Evaluation Optimization Using Code-Modifying LLM Agents
arXiv:2511. 00802v2 Announce Type: replace-cross Abstract: With data-driven development now widely adopted, online A/B testing is an established method for measuring the effects of new technologies.
arXiv AI
Sep 10
AgentIdeaBench: Benchmarking Scientific Ideation in the Agent Era
arXiv:2609.07611v1 Announce Type: new Abstract: Scientific ideation is the capacity to formulate novel and testable hypotheses from scientific evidence, and autonomous AI scientists depend on it. Exi...
arXiv Machine Learning
Jul 7
MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI
arXiv:2605. 08678v3 Announce Type: replace Abstract: Modern AI progress has been driven by ML methods that are generalizable across settings and scalable to larger regimes.