The paper proposes a new weak-to-strong routing strategy that uses model disagreement—measured directly from model-generated responses—to decide when to forward a query to a stronger model, eliminating the need for costly ground‑truth supervision. By characterizing the optimal routing policy and deriving a principled approximation based on disagreement, the authors show that their method balances routing cost and response accuracy across both agentic and single‑turn tasks. Experiments demonstrate that disagreement‑guided routing performs competitively, and can even surpass methods trained to predict correctness when such signals are available.
arXiv:2607. 22465v1 Announce Type: cross Abstract: Routing to select large language models (LLMs) with different cost-quality trade-offs has become a fundamental deployment feature of enterprise AI.
AgentCollab introduces a self-evaluation-driven collaboration paradigm for large language model agents, dynamically coordinating models of varying reasoning capacities during task execution. By using the agent’s own self-reflection to decide when to elevate to a stronger model, it avoids reliance on external routing modules and allocates additional reasoning budget only when recent failures indicate difficulty. Experiments on multi-step agent benchmarks demonstrate that this two-level small-large model setup consistently improves the accuracy-efficiency Pareto frontier of LLM agents.
Reasoning in language allows foundation models to spend more test-time compute on hard problems, such as those requiring decomposition, constraint tracking, and prediction of future consequences. Whet...
The paper introduces $R^3$, a post‑training method that converts vision‑language models into robotic reasoners by first mid‑training on expert reasoning traces and then refining them with single‑step rubric‑based reinforcement learning. $R^3$ enables free‑form language reasoning to guide low‑level manipulation policies, improving exploration, generalization, and performance on long‑horizon tasks in Language Table and simulated bimanual grocery packing benchmarks. The approach outperforms instruction‑only imitation learning baselines and demonstrates that natural language reasoning can serve as a test‑time compute mechanism for steering robotic actions.
arXiv:2607. 11399v1 Announce Type: cross Abstract: Large language model agents are increasingly executed not by a single model call, but by an execution harness that manages observation, context, control, action, state, and verification.
arXiv:2512.01946v4 Announce Type: replace-cross Abstract: Robust robotic manipulation requires reliable failure detection and recovery. Although recent Vision-Language Models (VLMs) show promise in r...
FlexRouter is a routing framework for large language models that explicitly models model complementarity to maximize answer coverage. It formulates routing as a coverage-oriented subset selection problem and uses Determinantal Point Processes to capture both competence and redundancy. During inference, a greedy strategy based on marginal log-determinant gains allows the router to adaptively determine subset sizes without a fixed budget, achieving higher coverage with lower redundancy on the RouterEval benchmark.
arXiv:2607. 00053v1 Announce Type: cross Abstract: Large language models (LLMs) embedded in multi-turn agentic harnesses are reshaping software engineering (SWE), but routing every task to a frontier model is wasteful when many issues admit cheap fixes.
The paper introduces a method for quantifying epistemic uncertainty in flow‑matching based generalist robot policies, such as vision‑language‑action models and world‑action models. By measuring velocity‑field disagreement across a small ensemble, the authors obtain better‑calibrated uncertainty estimates that can detect deployment failures and guide active fine‑tuning. Their SAVE approach reduces the need for expert demonstrations, improving real‑world task success from 39 % to 47 % while maintaining a fixed demonstration budget.
arXiv:2607. 20481v1 Announce Type: new Abstract: Local-cloud collaboration is a practical way to deploy large language models under resource constraints, but existing methods often rely on trained routers or collaboration-aware finetuning that tie routing behavior to a particular operating regime.
arXiv:2608. 15817v1 Announce Type: new Abstract: The growing ecosystem of large language models (LLMs) offers huge potential to optimize performance-cost trade-offs.
CF‑VLA introduces a two‑stage coarse‑to‑fine approach for vision‑language‑action policies, replacing multi‑step sampling with a coarse initialization that constructs an action‑aware starting point and a single‑step refinement that corrects residual errors. The coarse stage learns a conditional posterior over endpoint velocity to transform Gaussian noise into a structured initialization, while the fine stage performs a fixed‑time refinement. Experiments on CALVIN and LIBERO demonstrate that CF‑VLA achieves a strong efficiency‑performance trade‑off, reducing action sampling latency by 75.4 % and achieving an 83.0 % real‑robot success rate, outperforming existing NFE=2 methods and matching or surpassing NFE=10 baselines.