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Computation & Language / NLP
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Newest papers, page 179
FrugalEvo: Towards Cost-Aware LLM-Guided Program Evolution
LLM-guided evolutionary methods, such as AlphaEvolve, have emerged as powerful approaches for challenging computational optimization problems, such as circle packing.Language Models that Play Chess and Explain Their Moves
Modern chess engines are silent experts: they play at a superhuman level, but do not offer explanations for their play.Same Output, Different Gold: Measuring How Reference Choice Moves a Multilingual Benchmark Score
A benchmark score compares a system output against a reference, and methodological attention falls almost entirely on the first term.The Score Is Not the Structure: Brain Alignment and Cross-Lingual Transfer
Researchers often support the claim that a model shares structure with the brain, or across languages, by reporting a similarity score.OncoNoteBERT: A Foundation Representation Model for Natural Language Processing of Real-World Outpatient Oncology Notes
Real-world outpatient oncology notes contain specialised terminology, tumour staging expressions, treatment names, toxicity descriptions, and institution-specific de-identification markers that may…SYNLAT: Syntax-Aligned Text-Latent Compression for Chain-of-Thought Reasoning
Long chain-of-thought (CoT) traces impose substantial output-token costs.Training Numerical Intelligence via Auto-Diagnosis and Skill Discovery
AI agents are becoming increasingly capable of generating scientific code, but generating code is not the same as improving the algorithms behind it.Proxy Confidence: Auditing Black-Box LLM Agents with a Surrogate's Log-Probabilities
A deployed LLM agent emits tool calls, queries, and code that can be silently wrong -- by the time the error surfaces, the action has run.When Evidence Changes: Evaluating Memory Repair and Re-reading in Language-Model Agents
When documents supporting an agent's derived facts are revoked or replaced, should it repair memory or re-read current evidence?General Decision Models: Benchmarking and Insights Beyond Jev
General decision models, such as Jev, have recently emerged as efficient alternatives to LLMs for structured judgment and selection.A Step Towards Forgetting: Optimiser History and the Loss of Answer Mass
During fine-tuning, a language model can assign less probability to previously learned answers even when the current gradient acts to preserve that probability.BAIBAICHUCHU at the NTCIR-19 FinArg-3 Task: When Is Maximum Possible Profit Predictable from Investor Text?
The BAIBAICHUCHU team participated in the Social Media Subtask of NTCIR-19 FinArg-3, ranking Chinese investor posts by Maximum Possible Profit (MPP).Behavioral History Outperforms Descriptions of the Person for LLM Synthetic Personas
Large language models (LLMs) are increasingly used as synthetic personas representing survey respondents.Do Language Models Need a Trainable Input Embedding Table? Fixed Minimal Token Codes at 1.7B-Class Scale
A trainable input embedding table assigns each vocabulary item an independently adjustable vector.Slaying the Hydra: Interaction-Aware Circuit Discovery in Language Models
Localizing behavior to individual components of a language model is a central goal of mechanistic interpretability.Periscope: Extending Frozen Language Models Beyond Their Context Window
A language model reads long text in one quadratic forward pass, stops at the context window, and loses accuracy with length before reaching it.IdeaScientist: Orchestrating Agents for Grounded Scientific Ideation
Despite rapid progress in automating scientific research, generating promising and well grounded research solutions remains a central challenge.Representation-Aligned Auxiliary Supervision for Language Model Adaptation
Language models exhibit strong reasoning capabilities, yet adapting them to structured domains remains challenging and can yield inconsistent outcomes.SEER: Self-Evolving Event Reasoning and Retrieval for Time Series Forecasting
Real-world time series are frequently driven by exogenous events and structural shifts, rendering conventional forecasting based solely on historical numerical observations insufficient.LongSocialBench: Do Long-Context LLMs Understand Online Discussion Threads?
Long-context LLMs can now ingest entire online discussion threads, but understanding their social discourse requires more than reading a long document: models must track parent-reply relations…