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Unified Context Evolution

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Unified Context Evolution for LLM Agents

Announce Type: new Abstract: LLM-based agents can solve multi-step interactive tasks by combining reasoning with environment feedback, yet each episode starts from the same fixed context and any useful strategy discovered along the way is lost once the task ends. Existing approaches either limit learning to the current task or pool all experience into a single untyped store, without distinguishing knowledge types, tracking quality through use, or balancing what the library still lacks. We...

arXiv CS 8d ago

Model Context Protocols in Adaptive Transport Systems: A Survey

Announce Type: replace Abstract: The rapid expansion of interconnected devices, autonomous systems, and AI applications has created severe fragmentation in adaptive transport systems, where diverse protocols and context sources remain isolated. This survey provides the first systematic investigation of the Model Context Protocol (MCP) as a unifying paradigm, highlighting its ability to bridge protocol-level adaptation with context-aware decision making. Analyzing established literature, we...

arXiv CS 2d ago

Agent-R1: A Unified and Modular Framework for Agentic Reinforcement Learning

arXiv:2511.14460v2 Announce Type: replace Abstract: Large language models (LLMs) have rapidly evolved from single-turn text generators into the foundation of increasingly capable agents. As these agents take on more complex reasoning, decision making, tool use, and long-horizon tasks, reinforcement learning (RL) is becoming increasingly important for shaping their behavior. This shift is especially visible in agentic RL, where models must interact with tools and environments across multiple...

arXiv CS 8d ago

AMix-1: A Pathway to Test-Time Scalable Protein Foundation Model

arXiv:2507.08920v4 Announce Type: replace-cross Abstract: We introduce AMix-1, a powerful protein foundation model built on Bayesian Flow Networks and empowered by a systematic training methodology, encompassing pretraining scaling laws, emergent capability analysis, in-context learning mechanism, and test-time scaling algorithm. To guarantee robust scalability, we establish a predictive scaling law and reveal the progressive emergence of structural understanding via loss perspective,...

arXiv CS 1d ago

Toward Human-Centered Multi-Agent Systems: Integrating Cognition, Culture, Values, and Cooperation in AI Agents

Announce Type: new Abstract: The emergence of large language model (LLM)-based agents and multi-agent systems has enabled a shift from narrow task automation to more autonomous decision-making. Despite progress in language generation, planning, tool use, and coordination, most agents still treat intelligence as prediction, optimization, and task completion. Human environments are social and normative, where people reason under bounded rationality, communicate in culturally situated language,...

arXiv CS 1d ago

Development of a Structured Approach for Establishing Mission Engineering Requirements

arXiv:2606.05651v1 Announce Type: new Abstract: This paper addresses the question: How can mission effectiveness be systematically defined or approximated in the absence of customer requirements? Legacy requirements engineering frameworks presuppose customer input to define specifications but leave a gap in the process when stakeholder input is ill-defined or missing. Rapid build and development programs (such as military acquisition, space assets, infrastructure projects, etc.) often see...

arXiv CS 5d ago

MetaForge: A Self-Evolving Multimodal Agent that Retrieves, Adapts, and Forges Tools On Demand

Announce Type: new Abstract: Multimodal agents have achieved notable progress on complex reasoning tasks through tool use, yet remain limited by two issues: statically predefined tool inventories fail to generalize to unseen scenarios, and indiscriminate tool invocation incurs redundant cost and noise-induced errors. We propose MetaForge, a multimodal agent framework that learns when to invoke tools and how to evolve its toolset on demand. MetaForge factorizes agentic behavior into four...

arXiv CS 8d ago

How I Get Free Traffic from ChatGPT in 2025 (AIO vs SEO)

Three weeks ago, I tested something that completely changed how I think about organic traffic. I opened ChatGPT and asked a simple question: "What's the best course on building SaaS with WordPress?" The answer that appeared stopped me cold.

TechCrunch 188d ago

Deep learning four decades of human migration

Abstract Human migration is a fundamental driver of global demographic change, shaping population structure, labour markets and social policy across countries1,2,3. Although long-term migration patterns are often linked to economic development4, they can shift rapidly in response to shocks such as conflict, environmental crises and political change5. Despite its importance, migration remains difficult to measure consistently: existing data are sparse, concentrated in high-income settings and...

Nature 22h ago

A thalamus–brainstem attractor network drives history-biased decisions

Abstract Natural environments often change gradually, making it adaptive to bias decisions on the basis of the recent past — a phenomenon known as serial dependence1,2,3. Large-scale recordings during behaviour have identified that serial dependence is a common motif for decision-making, with neural representations of past experiences found throughout the brain4,5,6,7,8,9,10,11. However, it remains unclear whether this bias arises from dedicated neural circuits with history-specific...

Nature 22h ago