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Newest papers, page 186
SEDIMA: Cross-Run Hierarchical Insight Memory for Evolutionary Search Agents
Large language model (LLM)-driven evolutionary search is a powerful paradigm for automated program and algorithm discovery, yet existing systems are largely memoryless: each run explores from…Social bot detection in the age of ChatGPT: Challenges and opportunities
We present a comprehensive overview of the challenges and opportunities in social bot detection in the context of the rise of sophisticated AI-based chatbots.Hesitation Has a Geometry: Entropy-Trained Hyperbolic Probes for Sparse Activation Steering
When a large language model solves a mathematical problem, its reasoning is largely hierarchical, and the solution often branches at a few tokens where the next-token entropy is high.Trained Agentic Context Management
We study long context language models.Finding the Move Is Not Winning the Game: XiangqiBench for Closed-Loop Evaluation of LLM Agents
Static evaluations credit a language model for naming the right move, but an agent must carry a plan through to a verified outcome while an opponent responds.Evaluating and Improving the Robustness of Large Language Models to Input Sequence Variations
Large language models (LLMs) in production systems face prompt injections, trojans (backdoors), and manipulation of automatic quality metrics.Are you Synthesizing or Recalling? Evaluating LLMs on Algorithmic Code Retrieval
Large language models (LLMs) have demonstrated strong performance in code generation, where success depends on both recalling relevant algorithmic knowledge and reasoning about how to apply it.Counterexample Generation via Per-Theorem Symbolic Verifiers: When Imitation Hurts and Reinforcement Repairs
Large language models often solve a theorem forward yet fail to disprove a closely related false one: a falsification gap that supervised fine-tuning does not close and can actively worsen.FinDialogLens: Event Extraction over Multi-Party Dialogue for Missed-Trade Identification in Financial Chatrooms
Multi-party financial chatrooms are vital for sales-and-trading professionals, but their complexity makes manual recovery of missed trades infeasible: each Request for Quote (RFQ) is an event whose…CUEing User Simulators: Calibrated User Embeddings for Multi-Turn Benchmarking
Recent benchmarks rely on user simulators to evaluate AI agents in multi-turn interaction.Capability Scaling-Down Laws for LLM Compression
LLM compression reduces inference costs and memory requirements, but selecting a method and configuration remains largely empirical because comparable resource reductions can produce different…APDMem: Agent-Controlled Progressive Disclosure for Query-Adaptive Long-Term Memory
Personalized LLM assistants must recover sparse evidence from long conversation histories across queries of varying complexity.From Retrieval to Typed Decisions: Calibrated System One Models from Biomedical Sentence Encoders
Typed decision models answer schema-constrained questions about a text in one forward pass and return probabilities meant to be thresholded.Right Order, Wrong Scale: Auditing LLM Judges for Occupational AI Measurement
LLM judges are increasingly used to assess whether AI outputs meet workplace requirements, but agreement on response rankings does not establish agreement on acceptance rates or occupational…A generative-informed neuro-symbolic framework for syntactic ambiguity resolution: Evidence from Arabic DPs
Syntactic ambiguity poses a persistent challenge for Arabic NLP, particularly in morphologically rich nominal constructions where multiple structu6ral interpretations may be compatible with the same…Evaluating Multi-Dimensional Generalization of Large Language Models in Temporal Extraction Tasks
Time and event expression extraction are fundamental temporal reasoning tasks, but the problem remains difficult due to annotation ambiguity, domain sensitivity, and unstable model behavior.How Causality Bridges the Semantic Gap
Numerical measurements capture how a system behaves, but often leave the meanings of its variables unspecified.Fine-Grained Emotion Classification from Mobile App Reviews: An Empirical Study with Large Language Models
Fine-grained emotion classification of mobile app reviews enables requirements engineering activities that go beyond polarity-based opinion mining, including emotionally informed issue…MetaSteer: Context-Conditioned, nonlinear Steering via Attention-Projection Adaptation
Steering large language models typically relies on linear, context-independent interventions in activation space, an assumption that recent work has challenged and that can induce an information…Anchor-ECC: Local Integrity Checking for Watermarked LLM Outputs via Error-Correcting Codes
LLM watermarking has become an effective approach to distinguishing AI-generated text from human-written text by embedding detectable patterns during generation.