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8,685 papers
This work identifies the first token of the response as a structurally distinct target for diversification, largely overlooked in prior work, and finds that the first-token distribution is sharply concentrated and only weakly related to downstream correctness, as lower-probability candidates can yield similarly accurat...
This paper systematically investigates two principal test-time scaling axes of LLM agents: sequential scaling through extended interaction and parallel scaling through trajectory sampling, and finds that neither scaling axis can consistently yield meaningful gains from additional test-time compute in realistic environm...
STEPQuant is proposed, a spatial-temporal post-training quantization framework for Delta-rule recurrent states that allocates precision according to error magnitude and memory lifetime, and jointly fits key-row and value-column scales based on state distributions and key-row impact on output error.
GEB is introduced, a long-video memory framework that groups visually grounded observations of the same physical instance across clips into retrievable biographies while preserving the context of each moment and demonstrating improvements over prior memory frameworks in both multiple-choice and open-ended question answ...
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An explanation of how hybrid models of this sort can implicitly encode position at global NoPE layers is developed, and insights are provided for how to encode position in a way that can extrapolate to longer sequence lengths indefinitely.
Chain-of-thought traces are widely read as records of how models reach their answers, informing debugging, agent auditing, and claims about reasoning. Testing this interpretation is difficult because natural-language thinking traces are rarely mechanically verifiable. We revisit it in iGSM, a synthetic grade-school mat...
Bias auditing should be treated as an ongoing, multi-vendor process, rather than a one-time assessment, because gender-related biases are common in LLMs, but their direction and magnitude are highly heterogeneous, to the point that some models behave in diametrically opposite ways to others.
On-policy distillation (OPD) trains a student on its own generated responses using dense, token-level supervision from a stronger teacher. Vanilla OPD treats all teacher signals equally, assuming that the teacher's supervision is equally important for every token. However, teacher signals at different tokens may have v...
We evaluate an auditable long-term memory system on LongMemEval-S. Its retrieval chain uses hybrid candidate retrieval, cross-encoder reranking, coverage-first packet compilation, and deterministic reasoning scaffolds; an LLM is used only as a replaceable final reader. The chain places all gold sessions in the candidat...
AccHMR, the accuracy multiplied by HMR, which reports the reward in proportion to the accuracy, is proposed, which reports the reward in proportion to the accuracy, and on which the revised rules would have scored 0.6549, 5th.
This work is the first to unveil the critical role of the null space and harness it for model optimization, and uses a simplified analytical model about optimization to demonstrate why null-space can effectively reduce attention entropy, thereby improving the efficiency of reasoning.
PUMBA is introduced, a unified framework for trajectory-aware training that trains the denoiser on consecutive steps of policy-induced trajectories, passes information between steps, and optimizes them jointly by backpropagation through time.
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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
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