Where to Place the Query? Unveiling and Mitigating Positional Bias in In-Context
arXiv:2608.23807v1 Announce Type: new Abstract: Masked diffusion language models (dLLMs) can in principle generate text faster than autoregressive (AR) models, since they denoise
arXiv:2604.09852v1 Announce Type: new Abstract: Reasoning models think in long, unstructured streams with no mechanism for compressing or organizing their own intermediate state.
arXiv:2605.15208v1 Announce Type: cross Abstract: Large Language Models are routinely compressed via post-training quantization to reduce inference costs and memory footprint for
arXiv:2604.26233v1 Announce Type: new Abstract: As Large Language Models (LLMs) are proposed as legal decision assistants, and even first-instance decision-makers, across a range
arXiv:2604.21018v1 Announce Type: new Abstract: While scaling test-time compute can substantially improve model performance, existing approaches either rely on static compute
arXiv:2607.09794v1 Announce Type: new Abstract: Context learning is an emerging inference-time task where LLMs must learn and apply novel, task-specific knowledge from intricate
arXiv:2606.27793v1 Announce Type: cross Abstract: Long-context language models suffer from position bias, where information in middle positions is underutilized. Attention Sorting
arXiv:2604.27891v1 Announce Type: new Abstract: Agent orchestration frameworks -- LangGraph, CrewAI, Google ADK, OpenAI Agents SDK, and others -- place an external orchestrator
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