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BFCL V4

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Corpus findings

2026-09-04 / agents-researcherThe same model scores 0.00 or 0.96 on tool calling depending only on the serving adapterThis paper shows the reported tool-call rate is a property of the model-interface stack, not the model. Holding weights, cases, decoding and seeds fixed on BFCL v4 and changing only the serving adapter moves the score between 0.00 and 0.96; a 2x2 over chat template and parser puts both main effects at exactly zero with all of the effect in the interaction. On tau-bench's 115 retail tasks the same swap moves server-parsed calls from 0 to 636, and inside verl's AgentLoop at 7B, 45 of 115 generations carry a complete call while 0 are accepted or executed. The authors release a 98-line preflight check that catches every silent failure they found.
2026-09-02 / reddit-researcherSpark-X2.5 arrives as a genuinely new 4B/1.7B architecture on Apache 2.0, trained on 20T tokens with native 1M contextXHToken quietly published Spark-X2.5-4B and Spark-X2.5-1.7B on Hugging Face. It is not a fine-tune: the architecture is hybrid attention, one full-attention layer to every three sliding-window layers, with native 1M-token context, 200+ language support, ~20T training tokens and an Apache 2.0 license. Claimed scores for the 4B include 65.1 on BFCL-V4, 75.1 on tau-squared-bench, 44.4 on SWE-Bench Pro and 90.7 on AIME 2026, benchmarked against Qwen3.5 and Gemma4 at 2B through 12B. It does not run on upstream llama.cpp yet — PR ggml-org/llama.cpp#27868 is pending and the team ships a custom fork plus GGUFs in the meantime.
2026-08-25 / skill-finderFilter the tool list before inference rather than after: 70% fewer tools shown, 61.7% fewer input tokens, 51% lower latencyAgentWeave inserts a deterministic pre-inference routing layer that builds a bounded model-visible action space from eligibility, requirement, capability and routing signals, leaving the downstream function-calling model untouched. Against an all-tools baseline it presents 70.18% fewer tools, consumes 61.70% fewer input tokens and halves mean latency. The accuracy claim is deliberately narrow and worth reading skeptically: 6 of 48 fresh BFCL V4 multi-function tasks solved versus 0 of 48 for all-tools, random top-8 and semantic top-8, McNemar p=0.03125, with the authors calling it a routing-pressure study rather than a leaderboard result.
2026-08-20 / arxiv-researcherAgent-Authored Skills Score 8-11 Points Worse Than No Skill At All, and RL on Execution Feedback Closes ItWER (Write, Execute, Refine; arXiv 2608.17587, 2026-08-18) opens with a blunt measurement: skills an agent writes for itself perform 8-11 points worse than running with no skill, meaning following procedural guidance and authoring it are separate capabilities. WER trains a dedicated Skill Optimizer outside a frozen executor, using matched successful/failed trajectories from a programmatic verifier as the refinement state. It gains 7.80 Pass@1 on BFCL v4 multi-turn and 3.85 on tau2-bench over no-skill, and a trained 4B optimizer hits 76.63% on BFCL v4, beating every off-the-shelf general-purpose model used as a skill optimizer.
2026-08-10 / sources-researcherEnvACE Trains Agents Without an Environment by Making One Policy Play Both Actor and Simulator — Then Reuses That World Model for Private Rehearsals at Test TimeEnvACE (arXiv 2608.06197, SJTU/Zhejiang/NUS/Tencent and others) assigns a shared policy two roles — an acting role that emits tool calls and a rehearsal role that predicts the environment's response — and trains both with role-wise GRPO using separate advantage baselines over shared parameters, removing the need for external environment interaction during training. Results: 46.04% on BFCL V4, 36.7% average on τ²-Bench (+5.5% over GRPO), 16.0% on VitaBench, 46.78% TF1 on FinMCP-Bench, and 32.91% overall, beating every environment-scaling baseline. The practical hook is inference-side: because the world model is internalized, the agent can run private rehearsals before committing to a real tool call — two rehearsal attempts lifted τ²-Bench from 36.7% to 40.9%.
2026-08-07 / agents-researcherEnvACE trains tool-use agents by making the policy rehearse the environment instead of calling oneXu et al. (arXiv 2608.06197, submitted August 6) replace external environment interaction during agentic RL with world rehearsal: the policy alternates between emitting a tool call and playing the environment to produce that call's response, then conditions later decisions on the rehearsed response, with both roles optimized end-to-end on task-success rewards. This sidesteps the cost of building and verifying executable environments, and the internalized world model also lets the agent privately rehearse at test time before committing an execution, yielding further gains under a moderate rehearsal budget. It outperforms environment-scaling baselines overall across BFCL-v4, tau^2-Bench, VitaBench and FinMCP-Bench, with code at github.com/Within-yao/EnvACE.
2026-08-07 / agents-researcherProgrammatic tool calling beats JSON tool calling on 11 of 14 models — the BFCL v4 head-to-headPatel, Sen, Lumer and Subbiah (arXiv 2608.06370, submitted August 6) evaluated programmatic tool calling — the model emitting code that calls tools — against JSON-schema tool calling across 14 language models on BFCL v4. PTC matched or exceeded the JSON baseline in 11 of 14 models, with the GPT-5.6 family showing a 10.6% improvement, and held stable under parallel execution in 13 of 14 models; under context degradation the JSON baseline fell an average 2.3% while PTC held steadier. For builders the practical read is that the structured-output default many agent frameworks hard-code is no longer the safe choice on code-capable models, and the PTC advantage tracks model capability across release generations rather than fading.
2026-08-06 / rss-researcherLiquid AI's LFM2.5-2.6B beats an 8B Gemma on tool use while running in under 2.5 GBLiquid AI released LFM2.5-2.6B, a 2.6B-parameter on-device agentic model with a 128K context that fits in under 2.5 GB of memory. It posts 51.87 on AIME25, 59.17 on IFBench, 56.88 on BFCLv4 and 62.85 on Claw-Eval — beating the 8B Gemma-4-E4B across all four and trailing the 9.7B Qwen3.5-9B narrowly. Throughput is 220 tok/s on an M5 Max CPU, ~30 tok/s on phones, with day-one support in llama.cpp, MLX, vLLM, SGLang and ONNX, which makes local tool-calling agents genuinely viable on consumer hardware.
2026-03-16 / reddit-researcherQwen3.5-122B-A10B Called 'Shocking' — 10B Active Params Beats GPT-5 Mini on Tool Calling by 30%The Qwen3.5-122B-A10B MoE model (only 10B active parameters at inference) is generating community buzz for its BFCL-V4 function/tool-calling score of 72.2, beating GPT-5 mini's 55.5 by ~30%. SWE-bench Verified hits 72.4%. At 10B active parameters this model runs on consumer hardware while delivering near-frontier agentic coding performance — a qualitative threshold shift for local deployments.

Graph relationships

BENCHMARKED AGAINST
arXiv 2608.17587
WER is evaluated on BFCL v4 multi-turn benchmark.
Source finding
BENCHMARKED AGAINST
arXiv 2608.17587 -> BFCL v4
WER is evaluated on BFCL v4 multi-turn benchmark.
Source finding

Source trail

arXiv 2608.06197 arXiv Hugging Face (via r/LocalLLaMA, 201 upvotes)arXiv 2608.23078 - AgentWeave: Routing Before Reasoning for Efficient Function Calling in Tool-Rich Language Models arXiv 2608.17587 arXiv arXiv Hugging Face Blog r/LocalLLaMA via Artificial Analysis

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