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3,200 papers
This work presents ZipBench, a simple and low-cost BCM with theoretical error and rank-consistency guarantees that reduces the cost of both LLM evaluation and compact benchmark construction, lowering the barrier to broad LLM research for compute-constrained researchers.
This work introduces SynthSentry, a corpus-level, model-agnostic contamination signal requiring no access to the generating model, no generation history, and no synthetic labels, and frames screening as a data-curation defense rather than a post-hoc diagnosis and releases the scoring toolkit.
Experiments on seven heterogeneous handwriting benchmarks show that complementary supervision consistently improves training with page transcription alone, while joint multi-source training provides further gains on most datasets, and the proposed sparse expert model further improves the dense baseline on six of the se...
It is argued that the right resolution is state-dependent, and GACA, a critic-free estimator whose granularity follows an uncertainty-based criticality proxy is proposed, improves task success over GRPO and GiGPO at both 1.5B and 7B scales.
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This paper introduces Tasks over Application Manuals (TAM), a benchmark for evaluating long-horizon procedural reasoning and shows that current benchmarks may overestimate LLM reasoning ability and miss a key challenge: reliably following long, rule-based procedures.
DualMLC is proposed, a dual-branch framework that processes the same document through an autoregressive decoder-only language model and a bidirectional encoder, achieving state-of-the-art results on three widely used large-scale multi-label text classification benchmarks.
This work introduces context segmentation, a two-level agentic framework that divides complex exploitation tasks into manageable, contextually isolated sub-problems and demonstrates that for the E4B model, the strategy acts as an intelligent search, achieving competitive rewards with superior token efficiency compared...
We present ORQA, a method for testing occupation-level knowledge in large language models. Prior methods either map abstract LLM skills to occupations via task definitions or utilize expert knowledge which is difficult to obtain at scale and expensive. ORQA complements both of these methods by connecting O*NET occupati...
Small models are made more capable through distillation from a larger one that shares their tokenization scheme. However, do distilled byte and token models behave similarly in terms of scaling trends as compute and data increases? To enable this comparison, we introduce two variants to efficiently convert token logits...
This paper presents an open and modular transformer-based framework that detects and structures social tipping point evidence at the passage level, outperforming both a climate-focused model and untuned language models.
The results indicate that front-loading code understanding into a reusable, codebase-specialised vector store enables small local models to deliver grounded and repository-specific answers, making the agent well suited as a privacy-preserving development tool for in-house scientific codebases.
Assistants built on large language models are expected to write as their user would, and the dominant approach is single-channel: preferences summarised from conversation history and reinserted into context. This inverts the order of inference. Preferences are the task-dependent surface of a comparatively stable person...
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.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
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