The paper introduces SpecAgent, a protocol that lets a strong language model act as a reviewer rather than a generator for tool‑using agents. A weaker model drafts steps, executing read‑only tool calls to ground its proposals, while the strong model verifies the draft in a single prefill pass, committing the longest valid prefix and repairing any rejected step. Experiments on benchmarks show SpecAgent achieves near‑strong‑model performance while cutting memory traffic, wall time, and token generation.
arXiv:2607. 20531v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed over Model Context Protocol (MCP) servers, yet the benchmarks used to evaluate them score the final answer or a fixed "ground-truth" list of tools, both of which are fragile once the underlying data is live and stateful.
Speculative Macro Commit (SMC) is a runtime technique for tool‑using language‑model agents that separates an authoritative actor model from a faster speculative drafter model. The drafter predicts and executes future action chains on a snapshot, storing recurring multi‑action patterns in a macro library. When the actor’s next tool call aligns with a drafted action, SMC commits the pre‑executed steps, reducing latency by up to 18.59% on certain benchmarks while maintaining accuracy.
arXiv:2607. 03333v1 Announce Type: cross Abstract: LLM agents are becoming a common interface for research, coding, and question answering, yet their Thought-Action-Observation loop is often serial: the model reasons, emits a tool call, then idles the GPU until the result returns.
The paper introduces STEPGATE, an uncertainty‑aware handoff framework that evaluates each step of a small language model (SLM) agent and selectively escalates difficult steps to a stronger model. On a 52‑task single‑step benchmark, the Qwen2.5‑1.5B/7B pair achieved 82.7% task success with only 30.8% escalation, outperforming local‑only and random escalation baselines. In multi‑turn tests, STEPGATE reached 69.0% trajectory success and 84.0% action success while using only 30.0% cloud actions, demonstrating that step‑level escalation can close much of the performance gap to a stronger backend with fewer remote tokens.
arXiv:2609.38201v1 Announce Type: new Abstract: Long-running tools can dominate coding-agent latency: compilers, test suites, and repository commands take seconds to minutes while the agent idles. Th...
arXiv:2610.07817v1 Announce Type: cross Abstract: Organizations automating operational processes need more than a correct outcome: they need to predict how a process will run, know which one actually...
Speculative Macro Commit (SMC) is a runtime technique that speeds up tool‑using language‑model agents by having a fast speculative drafter model predict and execute future action chains on a separate environment snapshot. The drafter’s predictions are matched against a macro library of recurring multi‑action skeletons; when the authoritative actor’s next tool call aligns with the first drafted action, SMC commits the remaining pre‑executed steps to the official trajectory. Experiments with Qwen3.5 models show that SMC maintains overall accuracy while cutting latency by up to 18.6% on telecom benchmarks and 44.9% on AppWorld compared to sequential execution.
LEAP: Learning Efficient Action Proposals For LLM Agents proposes a method to speed up large language model agents by training a small 0.6B drafter to predict target actions accurately. The approach uses a latency framework that balances drafting, verification, and execution costs, achieving up to 60% faster end‑to‑end wall clock time without reducing task success. LEAP can be online trained, eliminating the need for prior trace collection and making it practical for real‑world deployment.
AutoTuneBench introduces a trustworthy measurement protocol for evaluating how large language model agents auto‑tune GPU kernels and serving engines. The benchmark addresses four failure modes—strawman baselines, machine‑dependent timing, saturated tasks, and infrastructure defects—by enforcing code‑frozen protocols, database validation, anti‑cheat checks, pre‑registered comparisons, and external result anchoring. Using this protocol, the authors demonstrate that previously reported speedups are inflated, revealing more modest improvements across different engines and machines.
arXiv:2608.23078v1 Announce Type: new Abstract: Large language models increasingly operate over large collections of tools, functions, APIs, and specialized agents. As the candidate action space grow...
arXiv:2604. 16706v2 Announce Type: replace Abstract: Automated evaluation of tool-using large language model (LLM) agents is widely assumed to be reliable, yet this assumption is rarely validated against human annotation.
arXiv:2610.31840v1 Announce Type: new Abstract: Tool-calling agents are mostly optimized for accuracy; in deployment, however, their growing cost is latency: every tool call is decoded autoregressive...