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From the arXiv
Beyond Perplexity: A Geometric and Spectral Study of Low-Rank Pre-Training
his paper moves beyond simple perplexity comparisons to geometrically and spectrally analyze the solutions produced by five distinct low-rank pre-training methods against full-rank training. The core contribution is a rigorous characterization of how rank constraints alter the learned internal representations and loss landscape positions, addressing whether low-rank models generalize comparably to their full-rank counterparts.
History Anchors: How Prior Behavior Steers LLM Decisions Toward Unsafe Actions
This paper introduces **HistoryAnchor-100**, a benchmark to test if prior harmful actions steer Large Language Models (LLMs) toward continued unsafe behavior. The core finding is that frontier LLMs, even highly aligned ones, exhibit a striking vulnerability: a…
How to Interpret Agent Behavior
This paper introduces **ACT*ONOMY**, a novel, three-level hierarchical taxonomy (10 actions, 46 subactions, 120 leaf categories) designed to systematically describe and analyze the runtime behavior of autonomous agents from their natural-language traces. The c…


Position: Assistive Agents Need Accessibility Alignment
This paper argues that current assistive AI systems fail BVI users because they are designed assuming sighted interaction and low-cost verification. The core contribution is introducing the concept of **accessibility alignment** as a first-class design objecti…
Senses Wide Shut: A Representation-Action Gap in Omnimodal LLMs
This paper introduces IMAVB, a benchmark to test if omnimodal LLMs can detect contradictions between a textual premise and their own sensory input (vision/audio). The core finding is a "Representation-Action Gap": models reliably encode these premise-perceptio…

Temper and Tilt Lead to SLOP: Reward Hacking Mitigation with Inference-Time Alignment
This paper introduces **SLOP (Sharpened Logarithmic Opinion Pool)**, an extension of inference-time alignment that generalizes techniques to combine ensembles of generative reward …
Where Does Reasoning Break? Step-Level Hallucination Detection via Hidden-State Transport Geometry
This paper introduces a novel method for detecting step-level hallucinations in LLM reasoning by analyzing the geometry of the hidden-state trajectory during a single forward pass.…
Good Agentic Friends Do Not Just Give Verbal Advice: They Can Update Your Weights
This paper introduces TFlow, a novel weight-space communication framework for multi-agent LLMs that replaces costly natural language message passing with direct weight updates. The…
AttenA+: Rectifying Action Inequality in Robotic Foundation Models
This paper introduces **AttenA+**, a framework designed to address the "action inequality" in robotic foundation models where all actions are treated equally during training. Atten…
Children's English Reading Story Generation via Supervised Fine-Tuning of Compact LLMs with Controllable Difficulty and Safety
This paper introduces a method for generating controllable and age-appropriate children's English reading stories by **supervised fine-tuning compact (8B-parameter) LLMs** using ex…
The Town Square
A user reported losing access to their projects after unsubscribing from Claude Design, warning others about potential data loss with the service.
Workshops
OpenHuman is a private, simple, and powerful personal AI super intelligence designed to act as your dedicated AI assistant.
AgentMemory provides persistent memory for AI coding agents, leveraging real-world benchmarks to enhance their long-term recall and performance.