2026-W29

This paper introduces a Cognitive-structured Multimodal Agent that addresses limitations in current unified multimodal models by externalizing visual information into an Episodic Visual Memory. This allows for selective retrieval of relevant visual context during reasoning, overcoming issues with long-horizon dialogues and token explosion. The agent's contribution lies in its novel architecture with distinct engines for perception, memory retrieval, and task execution, along with a new dataset generation method to facilitate training for improved cross-turn multimodal understanding and generation.

This paper introduces G-Frame, a multi-agent framework that uses game theory to train language models for scientific domains. By integrating Bayesian and team game principles, G-Frame forces models to internalize domain constraints, significantly reducing hallucinations. This approach leads to specialized models like OmniChem, which achieve competitive performance with state-of-the-art models while exhibiting drastically fewer errors.

This paper addresses the "Knowing-Using Gap" in LLM fine-tuning, where models memorize new facts but fail to apply them in reasoning. Using a novel "self-patching" technique, they identify that memorized knowledge is internally present but not correctly routed to computation-effective layers. This "knowledge-circuit misalignment" is the core reason for generalization failure, and their diagnostic finding leads to a practical strategy that significantly improves generalization performance.

UltraX addresses the diminishing returns of scaling LLMs by focusing on data quality. Its core method is an adaptive programmatic editing framework that refines pre-training data at scale by enabling fine-grained insertion, deletion, and modification. This approach improves data quality efficiently and reliably, overcoming limitations of existing rule-based and LLM-based methods.

WebSwarm addresses the limitations of single LLM agents in deep and wide web searches by introducing a recursive delegation framework. Its core method involves dynamically creating agentic search nodes that jointly decompose tasks, recursively expand search, and adaptively collaborate. This allows for more effective handling of complex research tasks by enabling agents to either solve objectives directly or delegate them to child nodes, improving both search depth and coverage.

This paper introduces SMetric, a novel LLM scheduling method for serving AI agents. It addresses the unique demands of agentic workloads, where complete responses are prioritized over per-token latency, and KV cache reuse is significantly higher. SMetric proposes a balanced session-centric approach that leverages global KV stores and intra-session locality to improve throughput without sacrificing KV cache efficiency, overcoming limitations of existing schedulers.

WCog-VLA addresses the limitations of reactive autonomous driving by introducing a dual-level framework for proactive driving. It achieves this by unifying world cognition and reasoning at a semantic level, incorporating 3D perception and agent tokens for dynamic understanding and Game-theoretic Chain-of-Thought reasoning. At a generative level, the Aligned Decoupled Diffusion Transformer synthesizes physically-plausible multi-agent trajectories, enabling more comprehensive world foresight.

This paper proposes a conceptual model for LLM-mediated workflows, representing workflow definitions, instances, and execution details as persistent knowledge objects. The core method uses Lisp-inspired concepts like symbolic forms and live-image thinking to distinguish between deterministic "derive" operations and LLM-driven "infer" judgments. The main contribution is a framework for semantic persistence, where workflows themselves become enduring knowledge, not just producers of it.

This paper introduces a novel framework for calibrating the eigenvalues of semantic embeddings from LLMs, treating them as density matrix predictors. By applying temperature scaling to these eigenvalues, the authors demonstrate improved uncertainty quantification. Their work establishes theoretical links between entropy and risk, deriving a specific calibration inequality for eigenvalues and proving that temperature scaling optimizes calibration.

This paper introduces TokenWall, a runtime defense framework for persistent AI agents. Its core method is to treat natural-language token flows (like memory updates or tool arguments) as the primary attack surface. TokenWall acts as a semantic firewall, auditing these flows to intercept unsafe behavior before it reaches privileged parts of the agent's runtime.

UniClawBench introduces a capability-driven benchmark for evaluating proactive AI agents in real-world scenarios. It addresses limitations of existing benchmarks by focusing on five core agent capabilities and offering 400 bilingual tasks designed for dynamic, multi-turn interactions. This approach allows for a more granular understanding of agent performance and failure points.

This paper introduces AUTOPILOT-VQA, a new benchmark designed to evaluate vision-language models' ability to understand safety-critical incidents in dashcam videos. The core method involves a dataset of structured questions about real-world driving events, covering various factors like weather, traffic, and accident details. This benchmark contributes by providing a standardized way to assess how reliably these models can reason about and answer questions related to hazardous driving situations.

DocMaster addresses the challenge of analyzing complex documents by preserving their hierarchical structure, unlike existing systems that flatten them. Its core method involves parsing documents into hierarchical trees and building a structure-aware semantic index. This enables more accurate document filtering and in-depth analysis, improving upon previous approaches by leveraging the inherent organization of documents.

OmniFood-Bench is a new benchmark designed to evaluate Vision-Language Models (VLMs) on their ability to perform complex food-related reasoning. It moves beyond simple classification to assess how well VLMs can identify ingredients, estimate quantities, and ultimately provide personalized health advice, addressing the "Systemic Information Asymmetry" between food appearance and its nutritional content. This benchmark's contribution lies in its progressive evaluation of these critical capabilities, enabling better development of VLMs for dietary management and healthcare.

This paper introduces the "Context Access Divide" (CAD) as a new dimension of AI inequality, distinct from existing measures of availability, quality, and quantity. The CAD highlights how the ability of an AI to dynamically retrieve relevant context from a user's knowledge corpus, versus requiring manual document attachment, significantly impacts AI utility for knowledge workers. This difference creates a qualitative threshold in AI usefulness, placing a heavy cognitive burden on users whose systems lack dynamic context retrieval.