CoCoDA co-evolves a typed compositional DAG of primitive and composite tools with the agent planner, using signature-based retrieval and a size-based reward to scale libraries efficiently and let an 8B model match or beat a 32B model on math and code benchmarks.
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8 Pith papers cite this work. Polarity classification is still indexing.
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ANNEAL uses Failure-Driven Knowledge Acquisition to localize faults, generate validated symbolic patches, and commit persistent repairs to a knowledge graph, achieving 0% recurring failure rates where baselines retain 72-100%.
NeuroMAS reframes multi-agent language systems as neural architectures where LLM agents learn coordination via reinforcement learning rather than predefined roles.
A single consistency instruction with harmful prior actions causes aligned frontier LLMs to select unsafe options at 91-98% rates in high-stakes domains, with escalation and inverse scaling by model size.
HELM raises long-horizon VLA success from 58.4% to 81.5% on LIBERO-LONG by combining episodic memory retrieval, learned failure prediction, and replanning, outperforming context extension or adaptation alone.
The paper delivers a unified executable benchmarking suite for tool-using agents that enforces a shared evidence-admission contract across web, code, and micro-task environments.
AppAgent lets large language models operate diverse smartphone apps via visual interactions and learns app usage from exploration or demonstrations.
SPREG detects logical failures in LLM long-chain reasoning through real-time entropy spikes and performs structured plan repairs using historical distributions, reporting a 20% absolute accuracy gain on AIME25.
citing papers explorer
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CoCoDA: Co-evolving Compositional DAG for Tool-Augmented Agents
CoCoDA co-evolves a typed compositional DAG of primitive and composite tools with the agent planner, using signature-based retrieval and a size-based reward to scale libraries efficiently and let an 8B model match or beat a 32B model on math and code benchmarks.
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ANNEAL: Adapting LLM Agents via Governed Symbolic Patch Learning
ANNEAL uses Failure-Driven Knowledge Acquisition to localize faults, generate validated symbolic patches, and commit persistent repairs to a knowledge graph, achieving 0% recurring failure rates where baselines retain 72-100%.
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NeuroMAS: Multi-Agent Systems as Neural Networks with Joint Reinforcement Learning
NeuroMAS reframes multi-agent language systems as neural architectures where LLM agents learn coordination via reinforcement learning rather than predefined roles.
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History Anchors: How Prior Behavior Steers LLM Decisions Toward Unsafe Actions
A single consistency instruction with harmful prior actions causes aligned frontier LLMs to select unsafe options at 91-98% rates in high-stakes domains, with escalation and inverse scaling by model size.
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HELM: Harness-Enhanced Long-horizon Memory for Vision-Language-Action Manipulation
HELM raises long-horizon VLA success from 58.4% to 81.5% on LIBERO-LONG by combining episodic memory retrieval, learned failure prediction, and replanning, outperforming context extension or adaptation alone.
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An Executable Benchmarking Suite for Tool-Using Agents
The paper delivers a unified executable benchmarking suite for tool-using agents that enforces a shared evidence-admission contract across web, code, and micro-task environments.
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AppAgent: Multimodal Agents as Smartphone Users
AppAgent lets large language models operate diverse smartphone apps via visual interactions and learns app usage from exploration or demonstrations.
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SPREG: Structured Plan Repair with Entropy-Guided Test-Time Intervention for Large Language Model Reasoning
SPREG detects logical failures in LLM long-chain reasoning through real-time entropy spikes and performs structured plan repairs using historical distributions, reporting a 20% absolute accuracy gain on AIME25.