arXiv AI
Aug 13
Causal Agent based on Large Language Model
arXiv:2408. 06849v3 Announce Type: replace Abstract: The large language model (LLM) has achieved significant success across various domains.
Large language models
Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
arXiv Machine Learning
Aug 13
ODE-Based Transformer Decoders for Iterative Sign Language Translation
arXiv:2608. 11352v1 Announce Type: cross Abstract: Sign language translation has achieved strong results with Transformer architectures, yet recent improvements largely rely on scaling model capacity at the cost of increased computation.
arXiv Machine Learning
Aug 13
Robust Multi-Tier Infant-Centered Audio Understanding with Whisper via Structured Speaker Conditioning
arXiv:2608. 11587v1 Announce Type: cross Abstract: Recent advances in model design and self-supervised audio representations have improved speech and audio understanding, yet infant-centered naturalistic recordings remain challenging due to limited labeled data, low signal-to-noise ratio, and cross-family domain shifts.
arXiv AI
Aug 13
ExRole: From Team Trajectories to Executable Roles in Multi-Agent Language Models
arXiv:2608. 11949v1 Announce Type: new Abstract: Roles provide an interpretable interface for organizing language-model agents, yet most multi-agent systems treat them as hand-written prompt labels disconnected from learned behavior and parameter updates.
arXiv Machine Learning
Aug 13
Task- and dataset-specific information in protein language models
arXiv:2608. 12090v1 Announce Type: new Abstract: Protein language models (PLMs) have transferred the latest advances from natural language processing to computational biology.
arXiv AI
Aug 13
Motion-as-Prompt: Enhancing Motion Reasoning in Multimodal Large Language Models via Motion-Guided Cross-Frame Visual Prompting
arXiv:2608. 11655v1 Announce Type: cross Abstract: Motion-centric video reasoning is fundamental to interactive applications such as robotic manipulation and autonomous navigation.
arXiv AI
Aug 13
Uncertainty as a Planning Signal: Multi-Turn Decision Making for Goal-Oriented Conversation
arXiv:2604. 03924v2 Announce Type: replace-cross Abstract: Goal-oriented conversational systems require making sequential decisions under uncertainty about the user's intent, where the algorithm must balance information acquisition and target commitment over multiple turns.
arXiv AI
Aug 13
Learning to Persuade Exposes How Easily LLMs Abandon Correct Beliefs
arXiv:2608. 11624v1 Announce Type: cross Abstract: Persuasion is a core dynamic of natural language communication, shaping how large language models (LLMs) update beliefs, resolve disagreements, and reach decisions.
arXiv Machine Learning
Aug 13
Robustness of AI-Art Detectors under Generator Shift
arXiv:2608. 11643v1 Announce Type: cross Abstract: Text-to-image generative models have advanced rapidly, with modern Diffusion Transformer architectures producing images that are increasingly difficult to distinguish from human-created artwork.
arXiv AI
Aug 13
Social Chain of Thought: A Multi-Agent Architecture Grounded in Medical Differential Diagnosis Methodology
arXiv:2608. 11420v1 Announce Type: new Abstract: Medical diagnostic reasoning is a high-impact use case for LLMs that carries significant implications for the health and wellbeing of users.
arXiv AI
Aug 13
Domain-Aware Lightweight Spectral-Grouped Convolutions for Hyperspectral Fish Freshness Classification
arXiv:2608. 12227v1 Announce Type: cross Abstract: Hyperspectral imaging (HSI) offers nondestructive assessment of fish freshness by detecting biochemical alterations across spectral bands.
arXiv Machine Learning
Aug 13
Long-Horizon Forecasting of Complete Financial Statements with Forma
arXiv:2608. 11327v1 Announce Type: new Abstract: Specialist training beats generalist scale when forecasting financial statements.
arXiv Machine Learning
Aug 13
MMLA: How Memory Lets the Past Shape the Future
arXiv:2606. 28876v3 Announce Type: replace-cross Abstract: Proposal.
arXiv AI
Aug 13
Poor Man's Agentic Modeling: Simulating Large LLM-Agent Societies on a Laptop
arXiv:2608. 11215v1 Announce Type: new Abstract: Simulating societies of many large language model (LLM) agents is expensive, yet the questions asked of such simulations are usually macroscopic: phase behaviour, stylised facts, and scaling with the number of agents $N$, not the cognition of any single agent.
arXiv AI
Aug 13
Forecasting Side Effects of Activation Steering
arXiv:2608. 11227v1 Announce Type: new Abstract: Activation steering modifies a language model by adding a learned direction to its hidden activations, enabling targeted behavioral changes without retraining.
arXiv AI
Aug 13
From Monolithic to Modular: Segment-level Automatic Prompt Optimization
arXiv:2608. 11219v1 Announce Type: new Abstract: Automatic Prompt Optimization (APO) often rewrites prompts monolithically, which can improve one behavior while degrading others.
arXiv AI
Aug 13
Low-Interaction-Rank Learning: Unifying Multiplicative Dual-Encoder Heads
arXiv:2608. 11661v1 Announce Type: cross Abstract: A multiplicative dual-encoder network computes a real-valued output for a pair of inputs as the inner product of their separate encodings.
arXiv AI
Aug 13
When the API Speaks the Wrong Language: Revisiting Post-Training for Multilingual Tool Use
arXiv:2608. 11715v1 Announce Type: cross Abstract: The reliability of Large Language Models (LLMs) for API calling degrades in multilingual settings.
arXiv AI
Aug 13
Do You See What You Draw? A Semantic Closed-Loop Framework for Holistic Evaluation of Unified Multimodal Models
arXiv:2608. 11907v1 Announce Type: cross Abstract: As Large Vision-Language Models increasingly aim to integrate visual generation and understanding within a single parameter space, evaluating such structural unification in a cohesive manner remains a critical challenge.
arXiv AI
Aug 13
Evaluating LLM Generated Detection Rules in Cybersecurity
arXiv:2509. 16749v1 Announce Type: cross Abstract: LLMs are increasingly pervasive in the security environment, with limited measures of their effectiveness, which limits trust and usefulness to security practitioners.