All papers

Trains an advisor model on past failures to add dynamic, structured guidance to frozen language model agent prompts.

This survey paper reviews attacks, defenses, and evaluation methods across 135 studies on secure retrieval-augmented generation systems.

Implements a formal framework in Lean 4 to verify hybrid quantum and classical programs.

The paper evaluates the cross-lingual transfer, calibration, and translation sensitivity of fast typed-decision models on regional Indonesian languages.

The paper introduces an imperceptible image perturbation attack that manipulates alignment in vision-language models during preference optimization without altering text annotations.

Introduces PEBench, a benchmark and evidence-grounded framework to directly evaluate prompt enhancers for video generation without rendering full videos.

Pairs attack success rate with operative understanding rate to assess whether models recognize harmful intent during safety evaluations.

Introduces a multi-turn conversational benchmark to evaluate sales agents against resistant, psychologically grounded buyer personas.

The authors propose a post-training method enabling autonomous language model agents to organize experiments and carry out machine learning engineering tasks.

The paper introduces a maturity model that grades artificial intelligence agents across multiple capability levels, evidence stages, and safety gates.

The paper reviews retrieval-augmented generation paradigms and examines frameworks across retrieval, generation, and modular components.

The paper introduces benchmarks and summarizes winning solutions for city-scale audio-visual reasoning and spatial intelligence at the Perception Test 2026.

Evaluates whether automated prompt optimizers inadvertently cause regressions in unscored prompt constraints and requirements.

Trains language models to anticipate user replies and self-revise drafts before responding in multi-turn interactions.

Replaces frequent state memory updates in state-space models by caching inputs and periodically checkpointing state during decoding.

Evaluates the safety of ChatGPT health advice using real user conversation logs across four Global South countries.

The paper presents an empirical study of 654 real-world configuration bugs across ten retrieval-augmented generation systems, categorizing causes and fixes.

Proposes Attr-RAG, an attribution-guided retrieval-augmented generation framework to improve long-form scientific experiment design queries.

The paper introduces SightCAD, an active view selection framework optimizing camera viewpoints under occlusion constraints for parametric CAD reconstruction.

Introduces an evolutionary optimization method using multi-turn reflective mutators to improve production data agents and pipelines.

Designs a modular multi-agent pipeline separating search, verification, and reading for multimodal document question answering.

Proposes a calibration-free post-training quantization method that jointly optimizes weight and activation scaling for 4-bit model inference.

The paper introduces a method to reduce redundant external knowledge retrieval by estimating what a language model already knows.

The paper introduces a diagnostic protocol to evaluate how speech recognition errors affect structured tool calling in voice agents.

Presents ObliVul, a framework combining safety obligation modeling and counterfactual validation to filter false static analysis vulnerability alerts.

The paper proposes Vantage, a training-free framework pairing VLMs with 3D models to plan and synthesize relevant viewpoints for spatial reasoning.

Studies reflective prompt optimization in research agents, demonstrating that gains on held-out tasks can stem from prompt-level memorization.

Studies how accelerated decision-making models affect overall task completion, latency, and costs across embodied and mobile agents.

Examines error localization and credit assignment failures in critique-conditioned on-policy self-distillation for mathematical reasoning.

Uses diffusion language models to perform multi-target few-shot infilling across shared code context guided by demonstration examples.

The paper tests whether role-playing language models leak private or unobserved character information across simulated interactive scenes.

Surveys the foundations, evolution, practical applications, limitations, and future research directions of retrieval-augmented generation.

The paper introduces Memory Forcing, partitioning cache into archive and working banks to prevent mid-horizon history loss in streaming video generation.

Introduces a benchmark evaluating how well vision-language models predict unseen physical outcomes following scene interventions.

Introduces a framework allowing agents to rewind long-horizon environments to earlier states while retaining learned exploratory knowledge.

Investigates whether frozen recurrent networks can provide sufficient memory for partially observable reinforcement learning without end-to-end training.

The paper analyzes gradient dynamics in top-k distillation, showing that renormalization prevents optimization along directions controlling tail token probabilities.

This paper presents a case study of an interpreter co-creating a local computer-assisted interpreting tool using large language models.

The paper examines machine grading on undergraduate essays across three universities, finding models compress score distributions and vary across institutions.

Applies Bayesian and search-based hyperparameter optimization to tune retrieval and generation parameters in a RAG chatbot.

Introduces a differentiable token merging operator running in linear time with an exact sequence output budget.

Introduces an instruction-following benchmark and training recipe to improve goal adherence in full-duplex conversational models.

Proposes an evaluation framework to measure how LLM agents handle uncertainty and resolve conflicting knowledge during execution.

The paper analyzes how pruning and subsequent recovery stages interact, finding instruction-following collapses earlier than domain knowledge during model compression.

The paper introduces BRIDGE, a runtime intervention framework that detects and repairs broken information handoffs across dependency graphs in multi-agent workflows.

Proposes a multi-stage framework combining retrieval-augmented generation and natural language inference verification to reduce model hallucinations.

Proposes a graph-based token pruning method inspired by sheaf geometry to reduce prefill computation in multimodal large language models.

The paper meta-analyzes 25 years of automated verbal deception detection models and identifies a persistent accuracy ceiling around 70 to 75 percent.

Presents an execution-based evaluation pipeline using browser agents to verify language model solutions for no-code software bug reports.

Presents a multi-agent LLM framework that automatically tunes specific internal algorithm components while preserving overall neural network structures.

The tutorial examines machine learning engineering agents from a data management view, highlighting challenges in pipeline execution, data discovery, and debugging.

The paper tracks automatic speech recognition performance from 2021 to 2025, finding persistent accuracy disparities between standard American English and minority accents.

The paper presents an elastic vision architecture that dynamically reconfigures width and supports partitionable inference across heterogeneous edge devices.

This paper presents and evaluates a low-code architecture combining Ollama and Open WebUI for self-hosting retrieval-augmented generation systems.

The paper introduces a 7,000-task benchmark to evaluate how multimodal models selectively use or ignore conversational context.

Proposes a reinforcement learning framework that tracks external versus internal knowledge sources to reduce search agent fabrication.

Introduces a key-value cache compression method representing attention activations as sums of compact Kronecker products.

The paper presents an engineering framework combining hybrid retrieval, reranking, guardrails, and automated regression testing to improve retrieval-augmented generation in regulatory systems.

This paper presents a three-stage generation and repair framework to resolve API constraints and tensor shape errors in deep learning programs.

This paper audits gender and stereotypical bias in multilingual job-matching embedding models using robust statistical tests.