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3,243 papers
Q2D-Web is introduced, a large-scale agentic retrieval benchmark consisting of a 190M-document web corpus and 70k agentic search queries in ten languages, reformulated from real-world user queries in production systems, and benchmark 13 retrievers including lexical, dense, and late-interaction models.
This work presents an RS-specific formulation of the region selection paradigm, previously explored in natural-image MLLMs, and extends it to temporal change localization over multi-image sequences, and constructs a multi-task training and evaluation suite spanning localization, referring expression, visual grounding,...
This work provides a preliminary framework for low-resource Sinhala diachronic analysis, highlighting the trade-offs between model sensitivity and data availability.
Social interaction is central to children's language learning, but the effects of different forms of caregiver feedback are difficult to isolate in naturalistic data. We use child-like language models as controlled learners to test which forms of feedback support grammatical development. Small GPT-2-style models are pr...
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The results reveal a pattern distinct from prior findings on long-tail vulnerability during acquisition and retention: among facts that models already answer correctly, those associated with highly connected entities are more likely to be corrupted by neighboring updates, and updates to such facts propagate errors more...
This work introduces SLOWeave, an online scheduling method that selects the largest prefill chunk predicted to finish before the earliest active decode deadline, and proves that, whenever a decode-only iteration is feasible and the cost predictor is accurate, SLOWeave maximizes immediate prefill progress among decision...
Signed Rescue Routing is introduced, a budgeted routing method that predicts these two events separately and ranks requests by their difference and shows that predicting incremental value, rather than model uncertainty, is a simple and effective objective for efficient LLM cascades.
A platform-agnostic CPU baseline is established by evaluating five open-weight models below two billion parameters and revealing a reliability-resource trade-off for edge deployment.
This work introduces Coarse-to-fine Error-aware Dynamic Attention Routing (CEDAR), a coarse-to-fine method that keeps the language model frozen while preserving global coverage and derives an output-error bound governed by within-chunk key/value dispersion and uses it to allocate a variable refinement budget.
Large language models (LLMs) are increasingly used to generate media, but whether their content perpetuates gender stereotypes is unknown: standard benchmarks rely on selection-based formats rather than long-form generation, and surveyed baselines for local gender associations are scarce outside the West. We collect ge...
This work proposes a scalable reinforcement-learning-based approach for rewriting existing source texts into instances that are more difficult to translate for MT systems, using a large language model with Group Relative Policy Optimization (GRPO) and fine-tune a large language model with Group Relative Policy Optimiza...
CONDUIT, a training-free refresh policy that unifies single- and multi-image reuse as residual-stream restoration, is proposed, building on norm-weighted attention and an accessible pre-output cached-value-norm proxy, then applies empirical image-level relevance amplification before one global selection.
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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.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
We’re giving developers, enterprises, scientists, and consumers access to Gemini 4 Argon, our latest frontier model.
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