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LLM agents can now adapt their action spaces dynamically from natural language, bypassing the need for pre-defined finite fields and accelerating inference through compositional policies.
OPD may improve sampling efficiency, but it risks making previously solvable problems unsolvable, challenging the notion of true capability expansion in LLMs.
Screening precision for merchant risk control at WeChat Pay skyrockets from 92.0% to 97.5% with SeqLLM's innovative integration of behavioral-sequence modeling.
Multimodal context learning remains critically underdeveloped, with top models barely scratching the surface of effective performance.
OrthoPilot outperformed seasoned orthopaedic experts in diagnostic reasoning, achieving a 10.6% increase in management success for complex musculoskeletal cases.
BETA achieves state-of-the-art continual learning performance with just 0.05 million trainable parameters, outperforming traditional methods by 180–3000 times in parameter efficiency.
Ditch the slow, iterative zooming during MLLM inference: Region-to-Image Distillation lets you bake those agentic zooming benefits directly into a single forward pass.
