Update README.md

README.md

@@ -50,24 +50,10 @@ SAP-4B is a 4B-parameter agentic language model specialized in **long-horizon mu
 
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-### Performance Note
-- The paper has been submitted to EMNLP 2026, and its reported results use the benchmark evaluation setting available at the time of submission.
-- For BFCL v4 Multi-Turn: subsequent updates to the evaluation setting led to minor subset-level score fluctuations in the latest re-evaluation, while the overall average performance remains consistent with the paper's conclusions. Fluctuations are purely caused by evaluation pipeline adjustments (stricter matching rules for base/long-context subsets, more favorable calibration for robustness subsets), with no changes to the model or training data.
-- For τ²-bench: only the submission-version result is currently available; re-evaluation under an updated setting may produce slightly different subset-level scores.
-- The paper's core finding — that argument-provenance grounded SFT significantly improves long-horizon tool-use performance without requiring RL — remains fully valid.
-
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-
 ## Key Capabilities
 1. **Cross-turn argument grounding**: Consistently sources tool argument values from verifiable upstream context (prior tool returns, initial state, user messages) rather than hallucinating parameters.
 2. **Long-horizon task execution**: Handles multi-step tool workflows across long context windows, maintaining dependency chains across turns.
 3. **Deployment robustness**: Performs reliably in missing-parameter and missing-function challenge scenarios that mimic real-world imperfect inputs.
 4. **No single-turn performance degradation**: Multi-turn specialization does not hurt single-turn function calling performance, with small consistent gains on single-turn BFCL v4 splits.
 
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-## Intended Use Cases
-- Multi-turn agentic tool calling and function invocation tasks
-- Long-horizon workflow automation requiring sequential tool use
-- Research on argument-level dependency modeling for tool-use agents
-- Baseline for 4B-scale agent model development