Model Detail
Agents-A1-4B-NVFP4
—Agents-A1-4B-NVFP4 is an AI model with 4B parameters released by sakamakismile. Distributed under the permissive apache-2.0 license.
Agents-A1-4B-NVFP4 ships with 4B parameters. Total weight footprint is approximately 3.0 GB, which is the relevant figure when planning local-inference VRAM. The apache-2.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
Agents-A1-4B-NVFP4 is best fit for general-purpose AI workloads. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Agentic vs. Parametric World Models: Hybrid Planning for Reliable Language Agents
arXiv:2606.27806v4 Announce Type: replace Abstract: Language agents, such as ReAct, Reflexion, and RAP, implicitly predict how the world will change as they plan and act, using imagined state transitions to guide their decisions. These predictions are encoded in the agents' backbone LLMs as internal
Lost in Recovery: Demystifying Recovery Behavior of Computer-Use Agents via Goal-Oriented Action Replay
arXiv:2610.27954v1 Announce Type: new Abstract: Recent advances in large (vision) language models have enabled computer-use agents that automate digital workflows via fine-grained keyboard and mouse operations. However, such agents remain fragile in long-horizon settings, where errors accumulate and
MMedAgent-Plus: Scaling Multimodal Medical Agents across Modalities, Tasks and Workflows
arXiv:2610.27233v1 Announce Type: cross Abstract: Medical workflows often require multiple interdependent analyses, but existing specialist and generalist AI models are not designed to coordinate them. We introduce MMedAgent-Plus, a multimodal medical tool-use agent that combines a vision-language p
Agents-as-Peers: Bias-Controlled LLM Evaluation as Multi-Agent Peer Assessment
arXiv:2602.02589v2 Announce Type: replace Abstract: Large language model (LLM) evaluation commonly relies on fixed human-authored benchmarks or centralized human/model judges. Fixed benchmarks can age and enter model training corpora, while centralized judges introduce their own scoring preferences.
ReuF2F: Benchmarking Coding Agents on Formalized Undergraduate Research Problems
arXiv:2610.27046v1 Announce Type: new Abstract: As AI systems begin to resolve open research problems, reliable verification becomes increasingly important. Formal mathematics provides a trusted framework for mathematical reasoning, enabling agents to produce machine-checkable proofs while conductin
Does Multi-Agent Collaboration Pay When Agents Serve Users? Testing Agent Scaling in Dual-Control Settings
arXiv:2610.27655v1 Announce Type: new Abstract: Multi-agent systems (MAS) built from language models are deployed on the premise that collaboration beats a single-agent system (SAS), yet when it pays is unclear. The most systematic study to date gives a capability-saturation rule: coordination helps
