Microsoft Research

Mandol achieves a 5.4x speedup in retrieval and a 4.8x speedup in insertion, revolutionizing long-term conversational memory management.

ConflictScore reveals that language models often overlook conflicting evidence, leading to overconfident and inaccurate claims.

Readers find AI-generated translations "fine," but overwhelmingly prefer human translations for their clarity and immersive quality, despite being unable to reliably distinguish between the two.

D2D transforms conversational product search by cutting conversation times by nearly 30% while boosting accuracy and user satisfaction.

Prospective memory in LLMs is not just harder than retrospective memory; it reveals critical insights into a model's reasoning capacity and attentional robustness.

Disciplinary siloing in research is starkly revealed through a novel citation graph that links claims to their sources, reshaping our understanding of knowledge evolution in AI fields.

Cognitive diversity among developers leads to distinct interaction modes with programming assistants, revealing that one-size-fits-all solutions may fall short.

Tail latency in LLM serving can be cut by up to 50% without relying on length predictions, reshaping how we optimize inference performance.

Simple prompting techniques can transform LLMs into more reliable mirrors of human judgment, recovering the full spectrum of responses.

Express achieves a groundbreaking reduction in approximation error and memory usage for causal attention, outperforming existing methods and enabling more efficient long-context language modeling.

LLMs can nail trivia in English, but stumble in Indian languages – unless you throw in some code-mixing, which magically bridges the gap.

Multilingual LLM performance disparities aren't random noise: language features and model biases systematically explain up to 92% of the variance, revealing concrete targets for improvement.

Stop hand-tuning your retrieval pipelines: BRANE slashes costs by up to 89% while matching accuracy by dynamically configuring pipelines per query.

SkillOpt transforms agent skill development into a reproducible optimization process, achieving state-of-the-art results by treating skills as trainable parameters.

AI's impact extends beyond formal roles, subtly eroding crucial "invisible work" like mentorship and feedback, potentially stunting career growth within tech companies.

TwinGate stops jailbreaks by tracking malicious intent across anonymized, interleaved queries with minimal overhead, something previous defenses couldn't do.

Discrete diffusion models can be sped up by 14x by intelligently choosing which tokens to sample at each step, without sacrificing accuracy.

Surprisingly, a trie-guided decoding framework applied to smaller encoder-decoder models like T5 and BART can outperform much larger instruction-tuned models like LLaMA-3 and Phi-3 in in-document query auto-completion.

Token-level attribution struggles to pinpoint the causes of LLM failures in realistic settings, suggesting current interpretability tools may not be up to the task of debugging complex model behaviors.

RosettaSearch recovers up to 68% more structural fidelity in protein designs, transforming how we optimize sequences beyond traditional single-pass methods.

Forget hand-crafted templates: DUET learns to generate user and item profiles jointly, boosting recommendation accuracy by better aligning textual representations.

Autonomous web agents get a serious upgrade with WebXSkill, which lets them learn and execute skills with both code-level precision and human-readable guidance.

LLMs are twice as likely as humans to repeat the same support tactic in a conversation, but a simple RL reward for tactic novelty can fix it.

Gaze-tracking unlocks a new level of personalized AI assistance, enabling LLMs to infer user cognitive states and boost recall performance.

Generative recommendation systems can now adapt to evolving user behavior without catastrophic forgetting, thanks to a novel drift-aware tokenization method that selectively updates item representations.

Hypergraph modeling of patient visits, coupled with contrastive pre-training, significantly boosts medication recommendation accuracy and safety by capturing complex relationships missed by traditional graph-based approaches.

LLMs, even when prompted or fine-tuned, struggle to replicate the messy reality of human conversation, raising serious questions about their utility as proxies for social interaction.

LLMs' ability to fairly represent English dialects hinges on the quality of human consensus, revealing a fundamental challenge in improving performance for low-resource locales.

LLMs still can't automate real-world threat research, struggling with accuracy and nuanced expertise in a new benchmark derived from a world-leading company's CTI workflow.

LLMs writing long stories frequently contradict themselves on basic facts and timelines, especially in the middle of the narrative, highlighting a critical weakness in long-form generation.

LLMs can mimic your style, but your friends can still tell it's not really you, especially when it comes to your opinions.

LLMs can now more accurately answer questions on complex documents thanks to a new system that understands layout and hierarchical relationships between document components.

Imagine a world where web agents don't just click and type, but orchestrate complex tasks with the reliability of APIs – Web Verbs offer a path to that future.

Guaranteeing consistent communication between AI agents is now possible: a new certification protocol slashes disagreement by up to 96% by ensuring agents share a common understanding of terms.

LLM development teams often resort to workarounds and augmentation strategies when faced with the practical challenges of integrating domain experts, revealing a gap between ideal participatory design and real-world constraints.

By explicitly prompting for reflection on failure, ERL unlocks up to 81% better performance in complex RL tasks and 11% gains in tool-using reasoning.

Ditch the army of task-specific models: AdNanny shows a single, reasoning-centric LLM can handle diverse offline advertising tasks with improved accuracy and reduced manual effort.

LLMs can get a 12% performance boost in low-resource languages by using a new framework that tailors data refinement, synthetic text generation, and continual pretraining to each language's digital footprint.