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9,049 papers
Results show that entity-structured indexing and episode-level provenance provide a reliable foundation for long-term memory in agents, and EnSIMem achieves high answer accuracy while maintaining compact, evidence-focused contexts.
CAVEAT-Harness is developed, which targets failures and raises the optimal purchase rate by up to 80.0 percentage points, and targeted post-training further improves a smaller open model, to establish incentive robustness as a distinct challenge for delegated agents and diagnose failure modes.
Non-speech interference can change a speech representation without causing comparable task loss. We test eight frozen encoders on four tasks, adding non-speech sounds throughout recordings, during speech, or in pauses. Under whole-recording interference, embedding drift tracks task loss across seven sounds, with mean S...
ContraVis is presented, a visual analytics system for human-in-the-loop contradiction analysis in legal contracts that models contracts as typed paragraph graphs that combine explicit contractual references with semantic relationships between paragraphs.
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This work studies two questions: when can one model improve two objectives simultaneously, and how can many trade-offs be covered without training a separate model for each?
Text-output scores alone do not show whether quantization preserves performance on speech tasks whose target labels cannot be recovered from the transcript. We evaluate fixed mixed 4/8-bit Qwen2-Audio-7B-Instruct allocations averaging 6 and 7 bits per parameter on 508 English-to-German FLEURS utterances and on 512 RAVD...
A sequential low-rank adaptation protocol for cross-scale transfer that attains the best point-estimate MAE, RMSE, and macro-$F_1$ on both 0.6B and 1.7B backbones, outperforming target-only training and non-LLM baselines.
The study concludes that the diglossic use of SA and CA contributes to the growing body of research on digital discourse, unveiling that the digital age reshapes but does not erode diglossic boundaries, giving rise instead to a reconfigured digital diglossia.
It is shown that, for frozen off-the-shelf encoders and language models, identity is computed when both sentences share one forward pass, and is not a property of the embedding geometry those systems ship.
This work introduces a step-by-step token extraction procedure to extract causal shortcuts from data, and applies parallel prioritized masking on these tokens during training to enable efficient and accurate convergence to correct answers via causal shortcuts.
At test time, large language models (LLMs) can encode historical information in activation memory (i.e., KV caches) and parametric memory (i.e., updated parameters). While activation memory is generally considered effective for factual recall and parametric memory for learning new tasks, their interplay remains unclear...
Large language models (LLMs) have shown strong performance in creative text generation, yet their ability to produce culturally grounded and stylistically constrained literary forms remains underexplored. Prior work has focused largely on modern language varieties and poetry, while classical prose traditions such as ma...
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Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
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A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
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