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media Hugging Face Forums · 19h ago

Niodoo: A Local Runtime for Hidden State Steering of Frozen LLMs

Jason Van Pham has released Niodoo, a local runtime designed to steer frozen large language models through their hidden states. The project aims to correct last-step errors by injecting noise or "physics forces" during inference to break token loops. This approach allows smaller models to improve performance without fine-tuning, targeting specific failure cases like the Llama strawberry prompt benchmark. The system generates its own telemetry tags and utilizes TDA analysis to monitor internal model states for looping behavior. Van Pham developed this tool organically through months of self-directed research and red-teaming, emphasizing reproducible results from pinned hashes. The code is available on GitHub under the repository Ruffian-L/niodoo-hidden-state-steering.
arxiv arXiv cs.CL · 19h ago

Argus Benchmark Evaluates Uncertainty Quantification Stability Across Vision-Language Models and GUI Grounding Datasets

The authors introduce Argus, a benchmark designed to evaluate post-hoc uncertainty quantification for computer-use agents that translate vision-language model predictions into executable GUI actions. The study assesses 28 open-weight methods across four VLM agents and four datasets, alongside eight closed-source methods from three vendors where internal model states are inaccessible. Key findings reveal selective transfer stability, where uncertainty rankings remain consistent across different datasets for a fixed model but degrade significantly when moving between different model classes or observable interfaces. Among open-weight options, hidden-state and density estimation techniques demonstrated the highest stability, while specific regimes favored sampling-based scores or verbalized self-assessment. Within-model ranking transfer proved strong with Spearman rho values up to 0.969, whereas cross-tier transfer to closed-source vendors averaged only +0.08. The research further indicates that conformal click regions shrink radii by 40-60 percent upon calibration but suffer coverage degradation under interface mismatch. To support regime-aware selection, the authors release per-item records, calibration splits, UQ scores, and analysis scripts.
arxiv arXiv cs.CL · 20h ago

ToolBench-X: Benchmarking Tool-Using Agents Under Unreliable Environments

The authors introduce ToolBench-X, a new benchmark designed to evaluate large language model agents under recoverable tool-environment unreliability. Unlike existing benchmarks that assume clean and stable environments, this framework injects five structured hazard types: Specification Drift, Invocation Error, Execution Failure, Output Drift, and Cross-source Conflict. The dataset contains executable multi-step tasks across diverse domains with deterministic tools and canonical final answers for automatic evaluation. Crucially, every injected instance remains solvable through valid recovery paths such as retrying, fallback, or verification. Experiments reveal a substantial reliability gap where agents performing well with reliable tools often fail under these hazards. Further analysis indicates that failures stem from limited hazard diagnosis and ineffective recovery rather than tool-use volume or inference budget. Targeted recovery hints successfully recover many failed tasks, whereas test-time scaling yields more limited gains. These findings suggest that evaluation must shift focus from function-call accuracy to task completion in unreliable environments.
media r/LocalLLaMA · 22h ago

Colony: An Educational Simulation of LLM Attention Mechanisms Using Agent-Based Analogies

Colony is an educational resource designed to explain the attention mechanism of Large Language Models through simple analogies involving agents. The simulation places these agents within a board environment inspired by Conway's Game of Life. Each agent in the system represents a specific role within the self-attention block mechanism of an LLM. This visual approach allows users to observe how information flows and interacts during the attention process. The project is available as an open-source tool for those interested in exploring these concepts without complex mathematics. It serves as a fun and accessible way to understand the internal workings of transformer models.
lab Claude Code Releases · 23h ago

Claude Code v2.1.191 Release Notes

Claude Code version 2.1.191 introduces /rewind support, allowing users to resume conversations from before a /clear command was executed. The update fixes several critical issues, including background agents resurrecting after being stopped and scroll position jumping during streaming responses. It also corrects behavior where /voice displayed generic error messages and where /login URLs were truncated in Windows Terminal. Significant improvements enhance reliability for MCP servers by adding retry logic for transient network errors during capability discovery and OAuth flows. Headless environments now skip browser popups for OAuth, while sandbox network permissions are remembered for the session duration. Performance optimizations reduce CPU usage during streaming by approximately 37% through text update coalescing and mitigate long-session memory growth from the terminal output cache.
arxiv arXiv cs.AI · 1d ago

MetaPS: Adaptive Strategy Selection for Market Agents

MetaPS is a simulation-guided framework that enables market agents to adaptively select among programmatic strategies based on market states. It uses simulated markets to generate supervised training data, then selects strategies during inference to produce executable actions. Experiments show MetaPS outperforms fixed strategies and LLM-based agents, with compact models exceeding stronger API models in performance.
arxiv arXiv cs.AI · 1d ago

Self-Evolving Cognitive Framework for Embodied Scientific Intelligence

The paper proposes a self-evolving cognitive framework that uses causal world modeling to enable embodied systems to continuously refine their internal models through interaction. It integrates causal modeling, intervention-driven reasoning, and continual refinement, redefining embodied interaction as an epistemic process for causal discovery and knowledge acquisition. The framework supports a shift from predictive to epistemic intelligence, with a new benchmark for evaluating self-evolving embodied scientific intelligence.
arxiv arXiv cs.AI · 1d ago

LLM-Orchestrated Agent for SOI Directional Coupler Design

A large language model orchestrates the design of a silicon-on-insulator 2x2 directional coupler by proposing gap values and assessing convergence. The design is validated through eigenmode and FDTD simulations on a common 2D effective-index model, showing a consistent phase offset of 2.837(11) micrometers that is corrected in a closed-loop process. The final device achieves a 50/50 split with a cross fraction of 0.498, within 0.0017 of the target.
arxiv arXiv cs.AI · 1d ago

Grounded Scaling: Determinism as a Core Limit in Agentic AI

Agentic AI performance degrades exponentially in non-deterministic environments, with k-step success falling as δ^k when per-step determinism δ < 1. The paper introduces a framework linking environment determinism to task success, verifiability, and skill evolution, proposing a Supply Certainty Index and a five-level Determinism Maturity Model. It challenges prevailing views by identifying determinism as a binding constraint across compute, data, embodiment, and alignment.