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Newest papers, page 155
Memory Depth and Reconstructed Context Width: A Controlled Evaluation of Hierarchical Retrieval
Long-term conversational memory is becoming an integral component of modern LLM systems.Language Unalignability: Why Some Concepts Resist Cross-Cultural Benchmark Evaluation
Current evaluation of multilingual Large Language Models (LLMs) rests on an implicit Translation-Isomorphism Assumption (TIA): that semantic structures across languages are congruent and mutually…CoDe-LoRA: Mitigating the Orthogonality Dilemma in Continual Learning of LLMs via Knowledge Consolidation and Decoupling
Continual learning (CL) is essential for Large Language Models (LLMs) to sequentially adapt to evolving tasks.Foresight-over-Graph: Reasoning Beyond Local Horizons for Knowledge Base Question Answering
Large language models (LLMs) have demonstrated strong capabilities in question answering, yet they still frequently suffer from hallucinations on knowledge-intensive tasks.Knowing When Not to Answer: Cross-Domain and Multi-Turn Generalization of Latent Underspecification Signals
Large language models routinely answer questions that cannot be answered from the information given, and in dialogue they answer before enough has been said.Rethinking Cross-Tokenizer On-Policy Distillation: From Alignment Coverage to Supervision Reliability
On-Policy Distillation (OPD) trains a student on its own generations using teacher feedback.Agentic AutoRAG: RAG Pipeline Optimization through Reasoning-Driven Agents
Retrieval-augmented generation (RAG) is a widely used approach for grounding large language models (LLMs) in external knowledge.UNREAL: Unifying Retrieval and Long-Context with a Single Model
Long-context inference and Retrieval-Augmented Generation (RAG) handle evidence selection at vastly different scales, from a single long prompt to an entire corpus.Language-model ratings of depression reflect the rater more than the patient
Depression has no diagnostic blood test.Wiki-Talkie: Multilingual Benchmarking of Persona-Based Agents on Real-World Discussions
LLMs are increasingly deployed as autonomous agents in social environments, making it critical to study their ability to faithfully simulate human interactions.Toward Alignment Scaling Laws: A Framework and First Preregistered Measurements
Whether alignment gets easier or harder as models grow is often argued from isolated findings, as if alignment were one property.How High Is 0.6? Floors, Ceilings, and Headroom in Interpretability Probing
Probes are the workhorse of interpretability.DeltaTTT: Layerwise Optimization for Nonlinear Recurrent Memory
Sequential test-time training adapts a memory network through successive updates, each computing an inner-loop gradient based on the network's previous state.Latent space bias directions in LLMs capture confidence, not fairness
Activation steering has gained popularity as a lightweight inference-time debiasing technique for large language models.Have I Seen Enough? Frozen Video-Language Models Encode Evidence Readiness
Streaming video-language models must decide not only what to answer, but whether the evidence needed for the current question has arrived.Large language models are vulnerable to incidental information in clinical documentation and reasoning
Large language models (LLMs) are increasingly relied upon to support ambient documentation and clinical reasoning.Generative AI translations in high-stakes emergency messaging
Emergency messaging such as extreme-weather reports and earthquake instructions can involve high stakes, to the extent that translation errors can lead to tragic consequences.InterCorrect: Intersection-Aware Correction of Demographic Model Merging for Fair ASR
Automatic Speech Recognition (ASR) systems often show uneven performance across demographic groups, and errors can be especially difficult to address for speakers belonging to multiple demographic…Towards In-Parameter Memory Augmentation for Large Language Models
Recently Large Language Models (LLMs) and LLM-based agents increasingly need to incorporate knowledge acquired after pretraining, e.g., domain facts, user preferences, documents, and interaction…SquidAgent: Parallelize Wisely, Coordinate Efficiently
LLM-based agents solve complex multi-step tasks, but sequential execution incurs substantial latency.