Use this view to inspect the underlying evidence corpus. For ranked developments, decision posture and interpretation, use Signals.
Raw feed or Signals?
Raw feed is chronological evidence. Signals ranks and interprets material change.
Research proposes using reasoning graphs to attribute LLM authorship, moving beyond surface-level linguistic features to improve detection robustness.
New research proposes Latent Trajectory Discrimination to detect AI-generated text, shifting from static document analysis to dynamic generation pathways.
OmniaBench introduces a new benchmark for evaluating general AI agents across diverse scenarios, tools, and interaction formats.
Research explores evolving LLM evaluation rubrics from a single query using synthetic pairwise evidence, enhancing fine-grained model assessment.
Research explores multi-agent LLM framework simulating political coalition formation, addressing RLHF biases that prevent steadfast partisan behavior.
Research audits LLM-generated encyclopedia Grokipedia for political neutrality, comparing it to Wikipedia amidst claims of bias.
Research investigates if neural language models distinguish grammatical from ungrammatical strings, moving beyond probability-based metrics.
Researchers introduced Transformers with Temporal Middle-Layer Recurrence (T2MLR), a new architecture addressing limitations in Transformer reasoning.
Research explores using mask-aware policy gradients to apply reinforcement learning more effectively to Masked Diffusion Language Models (MDLMs).
Research proposes expanding LLM tokenizers in-place for pre-trained models to improve efficiency and reduce latency for underrepresented languages.
Research proposes a linear-time language identification classifier using compositional data analysis, improving efficiency over neural models.
Researchers introduced TikStance, a multimodal dataset of 161 TikTok political videos and 13,876 comments for stance detection.
Research on multimodal VQA in healthcare shows parameter-efficient adaptation performs well, but answer quality doesn't guarantee faithful explanations.
Research proposes a statistical self-consistency method for LLMs, aiming to improve reliability by enforcing probabilistic identities like the law of total probability.
Researchers propose LLM-T1D, an interpretable language model for closed-loop Type 1 Diabetes control, addressing trust issues in black-box RL systems.
Research explores Agentic AI and Model Context Protocol (MCP) for orchestrating power grid studies, integrating LLMs with numerical simulations.
MemoHarness introduces an adaptive agent harness design that learns from experience to improve LLM agent behavior and orchestration.
New research proposes Branching Policy Optimization (BPO) for LLM agents in sandboxes, improving upon RLHF-inherited rollout topologies.
Federated learning on clinical text can leak sensitive data via gradient inversion; tokenizer choice impacts privacy risk.
MonteRET is a new AI agent framework enhancing multimodal LLMs with multi-granularity knowledge retrieval for chest CT report generation.
Research developed and validated the Generative AI Reliance Types Scale (GenAI-RTS) to measure how students rely on generative AI in academic writing.
Research introduces SD-MAR, a new method using synthetic data and reinforcement learning to improve multi-image analytical reasoning in VLMs.
Research paper proposes an auditable single-system method to evaluate LLM honesty by using a game engine as ground truth, not model self-assessments.
Research claims standard hate speech detectors over-attribute 'hate' by conflating it with partisan or geopolitical hostility in social media content.
RetroAgent uses LLMs with structured memory for multi-step retrosynthesis planning, improving upon traditional tree search methods.
WrAFT, an automated writing evaluation system, uses LLMs like LLaMA-3.3-70B-Instruct and GPT-4o for modular scoring and feedback.
Research introduces Expanded Hyper-Connections (xHC), extending Transformer residual streams beyond current limits to enhance memory scaling.
Research analyzed 4,500 LLM-chatbot interactions to model user engagement dynamics in English as a Foreign Language (EFL) writing with "Penny."
A research paper analyzes 109 Qubes Security Bulletins (2011–2025) and Xen Security Advisories to measure public security records.
A research paper studies memory-augmented conversational agents across 24 participants and 10 sessions, assessing relational constructs like self-disclosure and perceived memory.
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