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6,274 papers
It is demonstrated that CoT encodes recoverable, token-level problem-solving information, offering new insight into how reasoning is represented and where it breaks down, suggesting complete reasoning chains are not always necessary.
This work derives a closed-form expression for this adversarial perturbation, bypassing the iterative inner optimization of adversarial training entirely and enabling linear-time evaluation in the state dimension, and shows that this expression approximates the exact minimizer of the value function over the modeled uncertainty set with second-order accuracy.
PeopleSearchBench, an open-source benchmark comprising 119 multilingual queries across four scenarios: corporate recruiting, B2B sales prospecting, expert search, and influencer discovery, finds that multi-source search agents significantly outperform single-domain systems, particularly in influencer discovery where the performance gap is largest.
This paper replicates and extends the system used in the AuTexTification shared task for authorship attribution of machine-generated texts, and tested newer multilingual language models and added 26 document-level stylometric features, using ablation, permutation importance, and SHAP analysis to assess feature influence.
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This work proposes a multi-stage alignment method that teaches models to recall and apply relevant business policies during chain-of-thought reasoning at inference time, without including the full business policy in-context.
This work proposes DesignAsCode, a novel framework that reimagines graphic design as a programmatic synthesis task using HTML/CSS, incorporating a Plan-Implement-Reflect pipeline, incorporating a Semantic Planner to construct dynamic, variable-depth element hierarchies and a Visual-Aware Reflection mechanism that optimizes the code to rectify rendering artifacts.
LSTR (Latent Sparse Transcoder Reasoning), a framework that turns sparse transcoders from post-hoc diagnostic tools into in-loop, intervenable transition components for latent reasoning, and suggests that sparse latent transitions can preserve the compression benefits of latent reasoning while making the resulting trajectories more inspectable and intervenable.
SPADE (Soil moisture Pattern and Anomaly DEtection), which is the first LLM-based framework specifically developed for soil moisture time-series analysis, is proposed, which is the first LLM-based framework specifically developed for soil moisture time-series analysis.
Rank-One Safety Injection (ROSI), a white-box method that amplifies a model's safety alignment by permanently steering its activations toward the refusal-mediating subspace, is proposed, suggesting that targeted, interpretable weight steering is a cheap and potent mechanism to improve LLM safety, complementing more resource-intensive fine-tuning paradigms.
Language-Guided Tuning is introduced, a framework that employs multi-agent Large Language Models to automatically optimize configurations through natural language reasoning to demonstrate substantial improvements over traditional optimization methods while maintaining high interpretability.
RNop represents a shift in mRNA optimization methodology: by infusing explicit and interpretable knowledge, the"black-box"mRNA design can be transformed into a predictable, explainable engineering problem.
This work develops a new SpFT framework, based on ideas from neural network pruning, that improves SpFT's memory efficiency by 20-50\% while matching the accuracy of state-of-the-art methods like LoRA's variants.
Machine Intelligence
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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