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Long-horizon agentic workflows require models to sustain repeated state-dependent actions all while the context grows, sub-task complexity changes, and new data arrives. Each situation represents an independent axis along which an agent may fail. An agent reconciling a long ledger, for example, must repeatedly read its...
While autonomous software engineering (SWE) agents achieve high benchmark resolution rates, these scores can mask exploitative behaviors---such as leveraging local Git histories, accessing upstream repositories, or recalling memorized solutions---rather than demonstrating genuine problem solving. We systematize and aud...
Voice Memory, a inference-only scheme for agentic speech recognition: at stream time, a frozen corrector reads a single per-domain memory and decides per utterance whether to act on the hypothesis or abstain and keep the 1-best, and transfers across corrector families and adds zero parameters to the inference path.
This work proposes an efficient method to add streaming ASR capabilities to an existing duplex S2S model by introducing a lightweight ASR head in parallel to the agent text head, enabling real-time user transcription while preserving full-duplex conversational capabilities including turn-taking and barge-in handling.
A practical training recipe for the normalized Transformer and its evaluation on modern hybrid Mamba-2--Transformer Mixture-of-Experts models shows that the 30B-total-parameter nGPT model reaches the same validation loss using approximately half as many training tokens.
This work introduces SHERLOC (Structured Hypothesis-driven Exploration and Reasoning for Localization), a training-free framework pairing a reasoning LLM with compact repository tools and self-recovery, without fine-tuning or multi-agent orchestration.
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