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Newest papers, page 153
APEX: Speculate smarter, not deeper
Speculative decoding reduces large language model inference latency by drafting multiple tokens before target-model verification, but its effectiveness depends on both the proposal mechanism and…Quantization Effects on Tool-Failure Recovery Vary Across Prompts and Evaluation Designs
Post-training quantization reduces the cost of deploying language-model agents, but its effect on recovery from temporary tool failures can depend on how recovery is evaluated.Persistent Memory in Multi-Agent LLM Inference: What It Costs, What It Buys, and When You Can Tell
Decomposing long-context inference across cooperating agents bounds the active KV cache per call rather than total evidence, which matters when KV-cache memory binds.ThinkFuse: Trajectory-Aware Test-Time Fusion for Small Reasoning Models
Small reasoning models (SRMs) have shown strong performance on complex reasoning tasks by generating extended chain-of-thought trajectories, but they often fail to recover once their reasoning…One Step at a Time: Trading LLM Autonomy for Process Predictability
Organizations automating operational processes need more than a correct outcome: they need to predict how a process will run, know which one actually ran, and inspect it step by step.$α$Transfer: Coefficient Transfer for Efficient Model Merging
Model merging offers a promising solution for combining multiple fine-tuned checkpoints into a single model through parameter arithmetic.Nucleus Speculative Decoding: Plausibility-Aware Verification Beyond Exact Distribution
Speculative decoding accelerates autoregressive generation by using a lightweight draft model to propose multiple tokens that are verified by a target model in parallel.Harness Engineering for Software Engineering via Modular Executable Dev-Primitives
Large language models (LLMs) equipped with terminal access have demonstrated strong capabilities in automating software engineering tasks.OMIT the Action: Measuring Framing-Invariant Omission Bias under Philosophical Disagreement
As LLMs increasingly assist in moral reasoning, omission bias, the tendency to prefer inaction even when equivalent framings reverse substantive outcomes, poses a significant risk of skewed…Dynamic Positional Attention Modulation for Parameter-Efficient Fine-Tuning of Large Language Models
Parameter-efficient fine-tuning (PEFT) has become a standard approach for adapting large language models to downstream tasks.Lost in the bf16 Cast: Exporting Ternary Language Models Can Revert Most Low-Learning-Rate Code Changes
Ternary language models such as BitNet b1.58, Falcon-E and BitCPM are fine-tuned with higher-precision latent weights and deployed as ternary codes produced by an export step that, in the labs'…ReFold: Training-Free Reversible Inter-Turn Context Folding for Long-Horizon Agents
Long-horizon LLM agents act on an append-only interaction history that is re-sent to the model at every step, so the context and its cost grow with steps until the sessions exceed the context window.Visual Abstention in Unified Multimodal Models
Unified multimodal models (UMMs) integrate understanding and generation, yet their generative behavior is rarely governed by what they understand about the task.Rethinking Faithfulness in LLMs: A Pairwise Context-Sensitive Perspective
Large language models (LLMs) are expected to answer questions faithfully based on the provided context, abstaining when the context information is insufficient to answer the questions.ARIA: Audio-Driven Melody-Tone Relation Modeling for Cantonese Lyric Authoring
Cantonese lyric writing requires close alignment between lexical tones and melodic pitch.Isotropic Yet Undecodable: The Sequential Content-Sufficiency Gap in Latent-Predictive Text Representations
We study sequential content sufficiency by investigating whether a representation retains the ordered target information available in its input.Pseudowords as probes: Large Language Models show little of the sublexical sensitivity that governs human pseudoword processing
Systematicity, the probabilistic mapping of form to meaning, permeates language at all levels, and sublexical cues have been shown to govern human pseudoword processing.Leveraging a four-quadrant approach for evaluating Redpine Science
Redpine Science gives models and agents a single access point to a wide range of peer-reviewed literature, queried directly through the Model Context Protocol (MCP) and an API.Hybrid Latent Attention for Looped Language Models
Looped language models apply the same stack of layers T times to each token, which deepens the model without adding parameters but multiplies its key-value (KV) cache by T. The larger cache limits…Confidence Reasoning Graphs: Structured Confidence Estimation for LLM Agents
When using an LLM agent in a consequential domain, making an informed decision about whether to trust its output or intervene requires calibrated confidence in the agent's success.