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9,568 papers
This design introduces a rewritable proposal that uses additional compute to improve expressivity while keeping the simple mask-based transition schedule of MDMs, and fine-tune Qwen2.5-7B and Qwen3-8B to obtain LED-\texttt{7B} and LED-\texttt{8B}, respectively.
Results identify reward-preserving training structure as a practical route to lower per-step cost in short-window RLVR, and single-problem evaluations provide supporting evidence of retained reasoning quality.
This work evaluates how accurately large Language Models predict the impact of self-generated ML experiment ideas before these are put into training runs to show that current LLMs have useful but limited intuition about which ML ideas will work.
This paper observes that when a safe model is recursively self-distilled on its own (safe) generations while receiving an additional non-harmful cue, it becomes increasingly misaligned across a diverse set of harmful queries; it is called Progressive Misalignment.
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This work tests whether learning a specific factual binding produces a later relearning advantage beyond familiarity with its entity, answer vocabulary, format, or query slot, and whether that advantage remains positive after adjustment for measured pre-relearning behavior.
It is concluded that horizontally scalable, log-based messaging, combined with fault-tolerant, in-memory and micro-batch computation and durable, replicated storage constituted the technical foundation that made real-time, ML-driven enterprise analytics feasible within this period and that this layered architecture con...
Recent advances in Language Model (LM) agents have increasingly focused on repository-level software engineering (SWE) tasks, leading to various agentic workflows, training methods and failure analysis. Interestingly, rather than directly generating or evaluating edit patches, these approaches first examine and improve...
Large language models (LLMs) offer a compelling approach to entity matching (EM) in label-scarce settings, yet their matching quality is sensitive to records with ambiguous or sparse attributes due to the absence of external factual grounding. Retrieval-augmented generation (RAG) directly addresses this grounding gap b...
Despite their advanced reasoning capabilities, state-of-the-art Multimodal Large Language Models (MLLMs) still lack a core component of human intelligence: the ability to "read the room" and assess deception in complex social interactions. To rigorously evaluate this limitation, we introduce Multimodal Interactive Dece...
This paper investigates the influence of LLM-generated content on term-based retrieval models, and reveals that LLM-generated texts exhibit smoother high-frequency and steeper low-frequency Zipf slopes, higher term specificity, and greater document-level diversity.
Retrieval-Augmented Generation (RAG) has advanced repository-level code completion by retrieving external code snippets, API documentation, or type definitions to expand the model's contextual awareness. However, under the finite token budget of the context window, existing retrieval-augmented methods often saturate th...
Human quantitative researchers mine alpha through an iterative process: they derive hypotheses from financial knowledge, implement and test them as factors, and use the resulting evidence to guide subsequent research. Yet automated alpha mining faces three key bottlenecks: noisy financial knowledge is not directly acti...
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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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