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Dialect Matters: A Dialect-Aware Baseline for Igala-English Machine Translation
The massive permeability of intelligent systems in every sphere of human activities have greatly enhanced clarity, ease of work, and increased general global understanding which has significantly…When Do Summaries Become Instructions? Unsupported Policy in LLM Agents' Self-Written Handoff Summaries
Long-running LLM agents compact their context, replacing most history with a summary.Replicability, Reproducibility, and Robustness in Political and Social Science Research with Large Language Models
This chapter addresses the central reproducibility problem of research with large language models.Beyond accuracy: what AI assurance needs to establish in public services
Public bodies increasingly rely on AI-produced evidence, including classifications, summaries and assessments.ALTO: African and Levantine Tech-Facilitated Gender-Based Violence Corpus
Tech-facilitated gender based violence (TFGBV) is a pervasive online harm affecting women and gender minority groups through its many direct and covert forms.Reasoning over Non-Parametric Memory in Multilingual LLMs: A Cognitive-Based Analysis of Relational Knowledge
Retrieval-augmented generation (RAG) assumes that language models can reason over relations stated in their context rather than fall back on associations memorized during pretraining.Turns Matter: Advancing Streaming Video RL via Turn-Level Credit
Streaming video understanding requires models to repeatedly decide whether to respond or continue observing.Template Gap Reconstruction for Shell Output Simulation with Small Language Models
Small language models offer a compact basis for shell simulation without executing submitted commands, but full-output decoding requires them to predict recurring utility structure alongside content…Ranking Agreement Does Not Reveal Paragraph-Edit Agreement in Two Released Paper Acceptance Predictors
A fast acceptance classifier could select revisions for a slower evaluator.Measuring Risk, Not Harm: Detecting Psychological Harm Risk Factors in LLM Conversations
Psychological harms from interactions with large language models cannot be monitored directly because they may emerge over repeated interactions and depend strongly on subjective individual user…LLMs Don't Know What They Know: Benchmarking Temporal Self-Knowledge for Financial Backtesting
Financial backtests of large language models (LLMs) are meaningful only if the model did not already know the outcomes it is asked to predict.SAUCE: Summary Analysis Using Conversation Entailment
With the growing need for evaluating Large Language Models (LLMs) and their applications to speech, challenges persist in summarizing and evaluating conversations that lack a clear end goal.LooM: Loop-Invariant Memory for Causal Encoder-Decoder Language Models
Looped Transformers increase effective depth by re-executing shared layers, but three limitations hinder their adoption: memory grows with the loop count, compute can only be adjusted in whole-block…Before You Continue: Evaluating Time-Sensitive Safety Awareness in Conversational Assistants
Large language model (LLM) assistants increasingly help users with everyday activities that involve time-dependent constraints.Persona Perspectives: Adopting Viewpoints Reorganizes Representations in LLMs
Common methods for monitoring features in large language models (LLMs) such as linear probes and sparse autoencoders (SAEs) assume that features maintain a consistent representation across contexts.LongBanana: An Expert-Verified Benchmark for Long-Context Multi-Reference Image Synthesis
We introduce LongBanana, to our knowledge the first expert-designed and expert-verified benchmark for long-context multi-reference image synthesis in the age of agent-driven image generation.Balancing hierarchical representation and quantization: A performance trade-off in text embeddings
Hierarchical text embedding models offer variable-length representations, while quantization reduces numerical precision; both aim to improve computational efficiency.SpecCompact: Accelerating LLM Agent Context Compaction with Slot-Parallel Semantic Speculative Verification
LLM agents use context compaction to manage growing interaction histories, but summarizing long histories with large models can introduce substantial delays.A Framework for Context Enrichment and Selective Robustness in LLM Social Agents
Researchers use large language models as agents in social simulations, where agents are prompted with observations and background information intended to shape their actions.Interactive Backdoor Attacks in LLM-Based Proactive Dialogue Systems
LLM-based proactive dialogue systems provide goal-directed support through sustained interaction.