NNN decoding selects documents via non-negative elastic net reconstruction of the query embedding, with a theorem showing it strictly dominates dense retrieval on correlated corpora and experiments showing gains over inner-product baselines.
Efficient and scalable estimation of tool representations in vector space
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AsyncFC decouples LLM decoding from function execution via symbolic futures, enabling overlap and parallelism to reduce end-to-end latency on function-calling benchmarks while preserving accuracy.
Plan-and-Act trains a dedicated Planner on synthetic plan-annotated trajectories to generate high-level plans that an Executor follows, reaching 57.58% success on WebArena-Lite and 81.36% on WebVoyager.
citing papers explorer
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Non-negative Elastic Net Decoding for Information Retrieval
NNN decoding selects documents via non-negative elastic net reconstruction of the query embedding, with a theorem showing it strictly dominates dense retrieval on correlated corpora and experiments showing gains over inner-product baselines.
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Concurrency without Model Changes: Future-based Asynchronous Function Calling for LLMs
AsyncFC decouples LLM decoding from function execution via symbolic futures, enabling overlap and parallelism to reduce end-to-end latency on function-calling benchmarks while preserving accuracy.
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Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks
Plan-and-Act trains a dedicated Planner on synthetic plan-annotated trajectories to generate high-level plans that an Executor follows, reaching 57.58% success on WebArena-Lite and 81.36% on WebVoyager.
- FitText: Evolving Agent Tool Ecologies via Memetic Retrieval