AI agents
arxiv arXiv cs.CL · 2d ago

Moshi-Face: Full-Duplex Dialogue with Facial Generation

Moshi-Face is the first full-duplex spoken dialogue model that jointly processes audio and facial input, generating both speech and synchronized facial motion. It uses a VQ-VAE face codec to encode and reconstruct 3D head meshes from facial videos into discrete face tokens, and a Face Transformer module to generate these tokens non-autoregressively for real-time audiovisual output. Experiments show Moshi-Face achieves audiovisual alignment with low latency while maintaining original dialogue quality.

arxiv arXiv cs.CL · 2d ago

CFAgentBench: Benchmark for Autonomous Construction-Finance Agents

CFAgentBench introduces a reproducible, self-hostable environment with 1,014 machine-gradeable tasks across eight domains, grounded in real-world sources. It features 40 oracle-validated tasks with executable evaluators that assess functional correctness via state diffs and output regexes, including a money-movement guard requiring human approval for payments. A key finding is that top agents lose 43% of successes when repeating tasks under temperature-0 decoding, indicating single-attempt performance does not reflect real-world deployability.

arxiv arXiv cs.CL · 2d ago

Measuring Genuine Emergent Consensus in LLM Agent Societies

A new metric, coupling gain gamma, measures how agents adjust opinions when neighbors' views are perturbed. It reveals that frontier LLMs do not spontaneously polarize, and a diagnostic of final versus initial opinion shows that claimed emergent consensus in prior work involves model artifacts. Valid consensus emerges only when group-level, modality-matched coupling is considered, not single-neighbour interactions.

media Latent Space · 2d ago

AI Red Teaming and Prompt Injection Risks Explained

Zico Kolter and Matt Fredrikson, co-authors of the definitive paper on indirect prompt injections and authorities on the Mythos model, discuss the growing risks of AI security. They highlight that AI systems require a distinct security mindset, with agents introducing new vulnerabilities, and that specialized red-teaming AI can outperform humans in breaking models, making AI prompt injection breaches increasingly likely.

media r/LocalLLaMA · 3d ago

Same model, same prompt, 4 different agents produce varied code quality

A self-hosted Qwen3.6-27B model with identical prompt and hardware generated four different HTML/JavaScript solar system simulations. The agent scaffolding significantly influenced output: opencode produced clean, stable code with accurate physics; pi showed robustness and coordinate consistency; hermes offered visually appealing but physically flawed results; qwen code generated minimal, crude code. The results highlight how agent design shapes code quality, correctness, and stability despite shared model and prompt.