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7,777 papers
To study this phenomenon, SocioHack is introduced, a sandbox of 72 societal environments, and it is found that within these environments, reward hacking naturally emerges and leads to regulatory loophole discovery.
Confidence estimation (CE), i.e. quantifying the reliability of a model's prediction, has attracted great interest in the context of large language models (LLMs). However, most studies focus on English, ignoring the multilingual reality of LLM usage, while many CE methods degrade or require retraining across languages....
Court registries publish millions of decisions with structured metadata, which makes them an attractive source of labelled legal benchmarks: the court type, the form of the decision and its subject area come for free. We show that this convenience has a cost that registry-derived benchmarks rarely measure. We build Mul...
This work reframe schema linking as uncertainty-aware schema-need inference over multiple plausible SQL paths, where the system distinguishes required schema items from path-dependent uncertain ones and acquires evidence only where needed.
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Real applications provide the training setting closest to phone-agent deployment, but are difficult to reset, scale safely, and verify programmatically. Static screenshots and interaction trajectories preserve realistic evidence but cannot generate new experience. We introduce PhoneWorld, a trace-grounded framework tha...
Large language model (LLM) agents still struggle to effectively use long-term memory, with answer-supporting evidence often scattered across long conversational histories and buried in substantial irrelevant content. Existing memory systems commonly process memory into dedicated units before future queries are known an...
Results show that decoder-side adaptation alone can improve a meaningful subset of grounding failures under a fixed multimodal representation, while preserving broader multimodal capabilities.
Sequential diagnosis requires ranking diseases from a sparse intake under a public budget of follow-up questions, which is diagnostic triage with missing findings rather than screening of people who have no symptoms. Because laboratory series arrive irregularly and histories remain incomplete, the ranking must be updat...
The results suggest that the representation space in which diffusion operates should itself be treated as a first-class design variable for continuous diffusion in language models.
It is found that merely warning the agents of potential imposter presence is not beneficial: it degrades decisions when no imposter is present, provokes reactions ranging from self-accusation to scapegoating, inflates token use, and reveals to the imposter how it was uncovered.
This work proposes AIPO, an enhanced reinforcement learning framework that improves LLM reasoning through active multi-agent interaction during exploration and consistently improves reasoning performance and generalizes across different policy models, collaborator backbones, and RLVR algorithms.
The model's hidden state provides a way to read, steer, and check tool choice before a call is made, suggesting that the model's hidden state provides a way to read, steer, and check tool choice before a call is made.
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
A new language-processing tool could help identify the highest-risk individuals from natural language, enabling swifter interventions.
A Blog post by Liquid AI on Hugging Face
Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking are our most advanced live dialogue models yet, built for natural conversation.
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
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