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7,776 papers
Reference-Grounded Data Curation, a two-phase pipeline that extracts every supervised constraint from a reference translation that already satisfies it, ensuring feasibility by construction, is proposed.
Latent communication passes internal states between language models instead of decoded text, but higher receiver accuracy does not show that the receiver used the message content, so the interface can supply the gain while making the sharer dispensable.
ReMCTS, an execution-grounded, memory-augmented, LLM-guided MCTS-style search framework that organizes program candidates as tree states, retains branch-local debugging context, retrieves failure experience across branches, and distinguishes failed checks from unavailable evidence is presented.
S PRING uses SMT solver as a training-time verifier of intermediate reasoning steps to provide process-level supervision and introduces the notion of a novel reasoning step, namely, a step that is logically valid, consistent with the evolving reasoning state, and not already implied by previously accepted non-contradic...
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It is found that GitHub repositories linked in more recent ACL publications are unavailable at similar rates as in older publications, in parts due to an increase in empty and placeholder repositories.
Cardamom is presented, a micro-dialectal Arabic speech dataset designed to support fine-grained evaluation and adaptation of automatic speech recognition (ASR) systems, and the collection and annotation process is described, the micro-dialect inventory linguistically is motivated, and four multilingual ASR systems in z...
A simple reward model with warrant supervision is trained, training a simple reward model that outperforms both outcome-supervised baselines across nearly all response-selection comparisons, supporting RGDT-Bench's warrant supervision for RGDT reasoning.
Experiments demonstrate that, by iteratively exploiting richer hidden features and adapting to the evolving policy distribution, IPoET consistently outperforms both estimator- and verbalization-based baselines in-domain and achieves superior or comparable results across all out-of-domain metrics.
Reciprocal Guidance is introduced, a runtime draft-verify orchestration framework that adapts speculative decoding to varying serving loads and exploits spare compute capacity through verification-overlapped drafting at low concurrency, while dynamically allocating request-specific draft block sizes as the workload bec...
A culturally situated reward model that assigns rewards according to cultural appropriateness in open-ended social scenarios, and a collaborative multi-agent framework that instantiates implicit cultural norms into diverse social scenarios to construct culturally situated data are introduced.
A task-level bootstrap certificate that is valid in every regime the authors test while matching the naive certificate's coverage, and doubles as a self-training filter that lets a judge enter an unseen domain at in-domain strength with zero target training labels.
ABIDE (Apply-Bias Investigation via Decision-score), which adapts signal detection theory to Apply-vs-Suppress decision scores read directly from logits reveals a generation-induced Apply bias.
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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