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Computation & Language / NLP
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Newest papers, page 22
From Apparent Search Gains to Selection Failures: Auditing Test-Time Inference in Structured Workflow Planning
Test-time search can improve structured planning, but when methods differ in prompts, post-processing, and selection, it is unclear what an observed gain should be attributed to.Conic Certification of Transformer Attention
Attention is the computational core of large language models, yet the verifiers that bound how far an attention head's output can move under input perturbations give no guarantee on how loose their…TVP: TV is Almost All You Need for Speculative Decoding
Speculative decoding accelerates LLM inference with a drafter whose proposed tokens are verified by the target model in parallel.Saidzoda-Tajik-E5-Large: Fine-Tuning a 560M Embedder for Tajik Retrieval
Finding the passage that answers a question is the first step of most practical systems built on documents, and for Tajik we found no openly described model for it.Multi-Estimate Elicitation Does Not Uniformly Debias Language Model Judgments
Does eliciting five weighted estimates within a single response consistently improve numerical judgments?Sparse Autoencoder Attribution Graphs Are Decided at the Margin
Attribution graphs over the features of sparse autoencoders (SAEs) are used to predict what an ablation does and are compared across training seeds.Enhancing Language Model Reasoning with Structured Multi-Level Modeling
Inference-time scaling enhances a model's reasoning by extending its chain-of-thought (CoT).ElectroChemLLM: A Multimodal LLM Pipeline for High-Fidelity Electrochemical Informatics
Electrochemistry is central to solving many modern-day challenges, including energy storage systems for a sustainable future, and its advancement relies heavily on the data from past findings.Heirloom: Carrying a Phone's Personalization Across Base-Model Generations
On-device language models can learn a user's preferences through adapters trained on private data.Audit at the Precision You Deploy: Quantization Reshapes a Subliminally Learned Preference
Subliminal learning allows AI models to inherit hidden preferences from seemingly unrelated training data, such as number sequences.Precedent Labels and Facts Shift LLM Approval Decisions in Opposite Directions
When a language model decides refund or claim requests under written policies, the system can show it similar past cases.Learning Timeline-Preserving Speech-to-Speech Translation
Speech-to-speech translation (S2ST) increasingly preserves what is said and how it sounds, but not necessarily when speech and silence occur.Constructing Budgeted KGW Evasion through Token-Event Evidence Replacement
KGW watermarks bias sampling to accumulate keyed detection evidence.DuSE-Bench: Benchmarking Joint Reasoning over Evolving Dialogue and Documents
Persistent Web information assistants must combine evolving document facts with user requirements established and revised across sessions.ConstraintMatch: Compositional Image-Text Matching through Constrained Regional Evidence Selection
Local image--text matching can find strong evidence for individual concepts yet favor captions with incorrect attribute--entity bindings.CheXPoT: Post-Hoc Visual Verification and Correction of VLM-Generated Radiology Reports
Vision-language models (VLMs) have shown promise for chest radiograph report generation, but adoption remains limited because of hallucinated findings that are not grounded in the image.Steer, Don't Tell: Breaking Reasoning Plateaus with Reachability-Aligned Self-Distillation
Post training for large reasoning models remains insufficient on challenging problems, whose correct trajectories are hard to sample.ODC: Orthogonal Drift Correction for Improved Text-to-Image Semantic Alignment at Inference
Text-to-image models have achieved remarkable success in generating high-quality images from textual descriptions.Fewer Tokens, More Answers: Verifiable-Reward Tuning of an Ornith-Style VLM for Multi-View Driving QA
Reasoning-enabled model derivatives must turn intermediate deliberation into a usable answer within a deployment budget.Adaptive-Policy Distillation: Knowledge Injection under Information Asymmetry
On-policy distillation (OPD), which uses reverse-KL supervision on student rollouts to transfer a context-conditioned model's behavior into student model parameters for closed-book inference, has…