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9,568 papers
This work introduces Fast-Slow Thinking Reward Models (F/S-RM), a hybrid RM architecture inspired by Dual Process Theory that achieves state-of-the-art accuracy with an average score of 84.3 across benchmarks, while reducing token consumption by 22.5%.
Large language models (LLMs) exhibit strong performance on self-contained programming tasks. However, they still struggle with repository-level software engineering (SWE), which demands (1) deep codebase navigation with effective context management for accurate localization, and (2) systematic approaches for iterative,...
Rule-to-Tag (R2T), a framework that turns linguistic rules into the training signal for neural sequence taggers in low-resource languages, is introduced, a first instance of a broader paradigm the authors call principled learning (PrL): using explicit principles as a learning signal, rather than relying on example-base...
Choosing a Large Language Model (LLM) for a given task requires comparing many strong candidates, yet standard evaluation relies on costly annotations over fixed evaluation sets. To address this challenge, we develop SELECT-LLM, the first framework for active model selection of LLMs. SELECT-LLM aims to find a small set...
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LaSEr-Edit is proposed, a constraint-satisfying text revision method that can be applied to any LLMs, black- or white-box, and it is found that lightweight, task-specific energy-based models (EBMs) achieve error-localization performance competitive with or even better than that of much larger LLMs, while operating subs...
Granularity alignment, not algorithm choice, localizes when routing helps, and what category supervision buys when deployed through routing is quantified.
EpiKV is introduced, which combines hidden-state shifts with the model's recent query--key relevance to rank cached tokens without materializing the attention matrix, and matches or outperforms the strongest attention-based eviction baselines while running directly in vLLM with unmodified attention kernels.
It is shown that harness-aware post-training not only improves in-distribution performance but also enables agents to robustly adapt to OOD settings, highlighting the importance of harness-aware post-training under such shifts.
A post-hoc certification framework forparse autoencoders that provides a practical way to determine whether an SAE representation preserves enough of a frozen LM's predictive behavior to support certification of the original model.
Drop-by-Drop is proposed, a novel multi-bitwidth post-training quantization framework enabling inference-time precision control over LLM weights from a single quantized model, and it is proved that Gaussian sources are successively refinable under a weighted mean squared error distortion motivated by LLM loss functions...
Claw-SWE-Bench is introduced, a unified benchmark that enables researchers to systematically assess the capabilities and efficiency of general-purpose harnesses on software engineering tasks and guide the development of more capable and efficient harnesses.
EntmaxKV is introduced, an entmax-native sparse decoding framework that exploits sparsity before KV pages are loaded, and a Gaussian-aware entmax selector that estimates the entmax threshold from lightweight page statistics, adapting the selected budget to the score distribution.
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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.
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