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Hidden-State Probes Predict LLM Agent Failure Early, Saving Up to 47% Inference Compute

Researchers show that failure in LLM agent episodes is predictable early from the agent's internal representations. Lightweight per-round probes on hidden activations anticipate eventual episode failure as early as the first inter...

AI

o3-mini Achieves Higher Accuracy Than o1-mini Without Longer Reasoning Chains

A systematic analysis of reasoning chain length across o1-mini and o3-mini variants on the Omni-MATH benchmark finds that o3-mini (m) achieves superior accuracy without requiring longer reasoning chains than o1-mini. Accuracy gene...

AI

FirstResearch Framework Makes AI-Generated Scientific Questions Auditable

A new framework called FirstResearch introduces a structured Research Question Certificate to make the first research question proposed by LLM scientific discovery agents inspectable before downstream execution. The certificate re...

AI

RuBench Tests Coding Agents on Russian-Language Tasks From Real Repositories

A new benchmark called RuBench 1.0 evaluates product-grade coding agents on 25 repository-level tasks mined from recent fix commits in five live open-source repositories (aiohttp, aiogram, Laravel, NestJS, Fastify) spanning Python...

AI

HARC Couples Harmfulness and Refusal Directions for Stronger LLM Safety Alignment

Researchers introduce HARC (Harmfulness-And-Refusal Coupling), a fine-tuning method that pairs harmfulness and refusal directions across both prompt and response positions in the residual stream. The method achieves the strongest ...

AI

ROK-FORTRESS Benchmark Reveals Language and Geopolitics Shape LLM Safety Behavior

A new bilingual benchmark called ROK-FORTRESS shows that language and geopolitical context interact to shape large language model safety behavior in ways translation-only evaluations miss. Using the English-Korean language pair an...