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Computation & Language / NLP
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Newest papers, page 13
When Does Adaptive Halting Win? Selection Rules Matter in Looped Language Models
Looped language models can stop iterating early, and recent work shows that post-hoc readouts of the loop trajectory often rival the learned exit gate of the looped model Ouro.Outdated numeric values from Norwegian statutes and regulations in a further-trained model and its base model: a preregistered paired comparison of Borealis 2 and Gemma 4
We compare how often two released language models give outdated numeric values from Norwegian statutes and regulations: NbAiLab/borealis2-26b-a4b-preview (Borealis 2), which the National Library of…MemShift: Separating Detection from Repair in LLM-Derived Personal Memory
AI assistants store values that a language model derived from a person's records under the person's own definitions, such as their important contacts.Which Words a Model Fears: From a Refusal Direction to a Vocabulary-Wide Fear Lexicon
Whether an aligned language model refuses depends in part on which words a prompt contains, regardless of whether those words carry the request's meaning.VisCodeBench: a new benchmark dataset for Text-to-Vis reflecting real-world practice
Generating visualisations from natural language requests (text-to-vis) enables users to explore data more efficiently.Beyond Accuracy: How Quantization Affects Coding Agents
Agents are increasingly used for coding tasks, but the inference cost of large language models (LLMs) limits where they can run.EviCast: Forecasting Retrieval Gains Through Shared Response Calibration
Retrieval-augmented generation (RAG) extends large language models with external evidence, yet relevant evidence does not ensure that a generator can use it.Solver-Like Behavior in Large Language Models as Formalizers
In the LLM-as-formalizer paradigm, a large language model translates a problem into a formal program and leaves the search to a solver, gaining the solver's guarantees.When Rewards Depend on the Group, GRPO Plays a Tournament
Rewards for diversity, coverage and distribution matching make the reward of each sampled response depend on the rest of its group, and they are now routinely optimized with GRPO and RLOO.InteractionBench: Measuring Timing, Task Accuracy, Whisper and Volume, and Grounding in Real-Time Voice Systems
Most benchmarks for real-time voice systems focus on one aspect, such as turn-taking, instruction following, paralinguistic control, or conversational memory, each with its own data and set of…Diagnosing Model--Item Interactions in Language Model Benchmarks with LSIRM
Language models with similar benchmark accuracy can succeed on different items.As Expected? Efficient LLM Planning via Expectation-Observation Consistency Verification
LLM planning often relies on reactive executors (e.g., ReAct) that generate a single action at each step to remain grounded in the latest observations, incurring substantial inference cost.GigaSpeech 3: Scaling Multi-Dialect Arabic Speech Recognition with Weakly Labeled Data
Dialectal Arabic speech recognition remains constrained by the scarcity of dialect-resolved transcribed speech despite the abundance of web audio.Chain on Demand: Deferred Evidence Organization for Long-Term Conversational Memory
Long-term conversational memory faces a timing mismatch: write-time summaries and graphs organize history before a future query is known, while independent retrieval can allocate a limited evidence…Same Sentence, Different Construct: Identity-Swapped Rationales Reveal Measurement Non-Invariance in LLM Hate Speech Classifiers
Large language models are increasingly used as measurement instruments for socially consequential constructs such as hate speech, and are typically validated by their aggregate agreement with human…Ranked but Not Stopped: Detecting Prompt-Injection Hijacks from Typed Decision Models' Output Probabilities
Programs that use a language model as a classifier act on its most probable label.Tune the Baseline First: Most of Muon's Scaled Weight Decay Gain Is a Tuning Gap, but Its Small Real Advantage Grows With Model Size
Scaled weight decay for Muon (Muon-SW) has been reported to train language models more efficiently than constant decoupled weight decay, but such comparisons usually reuse one learning-rate (LR) and…PixelBench: Can LLMs Understand and Generate Images Pixel by Pixel?
Large language models (LLMs) can generate visual assets through text-based formats such as SVG and CAD, but these symbolic representations describe images using high-level primitives rather than…FindLink: Learning to Assess Scientific Connections from Source-Grounded Premises
Scientific discovery from the literature requires assessing connections among findings obtained under different study conditions.Re$^3$Rank: Listwise Reranking with Reasoning-Conditioned Retrieval Heads
Attention-based rerankers score each candidate by the attention mass that retrieval heads route to it from the question, but the question, serving as a fixed query, carries limited information for…