HFEPX Hub

CS.LG Papers (Last 30 Days)

Updated from current HFEPX corpus (Apr 27, 2026). 476 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Apr 27, 2026). 476 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequent quality control: Calibration. Frequently cited benchmark: MMLU. Common metric signal: accuracy. Use this page to compare protocol setup, judge behavior, and labeling design decisions before running new eval experiments. Newest paper in this set is from Apr 8, 2026.

Papers: 476 Last published: Apr 8, 2026 Global RSS

Cs.LGLast 30d

Researcher Quick Triage

This hub is best used for protocol triage and replication planning from abstract-level evidence. Quality band: Medium .

Analysis blocks below are computed from the currently loaded sample (60 of 476 total papers in this hub).

All Sampled Papers (60) Replication-Ready Only (10)

High-Signal Coverage

100.0%

60 / 60 sampled papers are not low-signal flagged.

Replication-Ready Set

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

Papers containing both `human_eval` and `llm_as_judge`.

10 papers are replication-ready (benchmark + metric + explicit evaluation mode).
0 papers support judge-vs-human agreement analysis.
0 papers report explicit quality controls (calibration/adjudication/IAA).

Primary action: Start with the top 2 papers in “Start Here”, then validate assumptions in the protocol matrix.

Need evaluators for this research workflow?

Post a Job →

Why This Matters For Eval Research

3.6% of papers report explicit human-feedback signals, led by demonstration data.
automatic metrics appears in 14.9% of papers in this hub.
MMLU is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

Most common quality-control signal is rater calibration (0.8% of papers).
Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.
Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.

Benchmark Interpretation

MMLU appears in 1.5% of hub papers (7/476); use this cohort for benchmark-matched comparisons.
DROP appears in 1.3% of hub papers (6/476); use this cohort for benchmark-matched comparisons.

Metric Interpretation

accuracy is reported in 14.7% of hub papers (70/476); compare with a secondary metric before ranking methods.
cost is reported in 7.1% of hub papers (34/476); compare with a secondary metric before ranking methods.

Researcher Checklist (Expanded)

Researcher Checklist

Gap: Papers with explicit human feedback

Coverage is a replication risk (3.6% vs 45% target).
Gap: Papers reporting quality controls

Coverage is a replication risk (0.8% vs 30% target).
Gap: Papers naming benchmarks/datasets

Coverage is a replication risk (9.5% vs 35% target).
Moderate: Papers naming evaluation metrics

Coverage is usable but incomplete (33% vs 35% target).
Gap: Papers with known rater population

Coverage is a replication risk (2.7% vs 35% target).
Gap: Papers with known annotation unit

Coverage is a replication risk (6.9% vs 35% target).

Strengths

Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.

Known Gaps

Only 0.8% of papers report quality controls; prioritize calibration/adjudication evidence.
Rater population is under-specified (2.7% coverage).
Annotation unit is under-specified (6.9% coverage).

Suggested Next Analyses

Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
Stratify by benchmark (MMLU vs DROP) before comparing methods.
Track metric sensitivity by reporting both accuracy and cost.
Add inter-annotator agreement checks when reproducing these protocols.

Recommended Queries (Expanded)

Recommended Queries

Judge vs Human Agreement Benchmark Slice: MMLU Metric Slice: accuracy Recent High-Signal Papers

Start with These 3

Use these when you need one protocol anchor, one benchmark anchor, and one recent comparison point before reading the wider hub.

Strongest protocol reference

Personalized RewardBench: Evaluating Reward Models with Human Aligned…

Highest protocol score with explicit human/eval signal plus Rewardbench.

Strongest benchmark reference

TraceSafe: A Systematic Assessment of LLM Guardrails on Multi-Step To…

Tracesafe-Bench with accuracy gives a fast comparison anchor.

Strongest recent paper

Paper Reconstruction Evaluation: Evaluating Presentation and Hallucin…

Useful for current practice scanning; published Apr 1, 2026.

Start Here (Best First 6)

Ranked for protocol completeness (human signal, benchmark + metric anchors, quality controls, and judge/human overlap).

Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization
Apr 8, 2026 · Citations: 0 · Score: 7.5

HF: Pairwise Preference, Rubric Rating · Eval: Human Eval, Automatic Metrics · Benchmark: Rewardbench · Metric: Accuracy
TraceSafe: A Systematic Assessment of LLM Guardrails on Multi-Step Tool-Calling Trajectories
Apr 8, 2026 · Citations: 0 · Score: 7.5

HF: Red Team · Eval: Automatic Metrics · Benchmark: Tracesafe Bench · Metric: Accuracy
Paper Reconstruction Evaluation: Evaluating Presentation and Hallucination in AI-written Papers
Apr 1, 2026 · Citations: 0 · Score: 7.5

HF: Rubric Rating · Eval: Automatic Metrics · Benchmark: Paperwrite Bench · Metric: Cost
Do Phone-Use Agents Respect Your Privacy?
Apr 1, 2026 · Citations: 0 · Score: 7.5

HF: Pairwise Preference · Eval: Automatic Metrics · Benchmark: APPS · Metric: Task success
Kernel-Smith: A Unified Recipe for Evolutionary Kernel Optimization
Mar 30, 2026 · Citations: 0 · Score: 6.0

HF: Critique Edit · Eval: Not reported · Benchmark: Kernelbench · Metric: Not Reported
RuleForge: Automated Generation and Validation for Web Vulnerability Detection at Scale
Apr 2, 2026 · Citations: 0 · Score: 5.5

HF: Expert Verification · Eval: Llm As Judge, Automatic Metrics · Benchmark: Not Reported · Metric: Auroc

Protocol Matrix (Top 12)

Use this to quickly compare protocol ingredients instead of scanning long prose.

Paper	HF Signal	Eval Modes	Benchmarks	Metrics	QC
Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization Apr 8, 2026	Yes Pairwise Preference , Rubric Rating	Human Eval , Automatic Metrics	Rewardbench	Accuracy , Helpfulness	Not Reported
TraceSafe: A Systematic Assessment of LLM Guardrails on Multi-Step Tool-Calling Trajectories Apr 8, 2026	Yes Red Team	Automatic Metrics	Tracesafe Bench	Accuracy	Not Reported
Paper Reconstruction Evaluation: Evaluating Presentation and Hallucination in AI-written Papers Apr 1, 2026	Yes Rubric Rating	Automatic Metrics	Paperwrite Bench	Not Reported	Not Reported
Do Phone-Use Agents Respect Your Privacy? Apr 1, 2026	Yes Pairwise Preference	Automatic Metrics	APPS , Myphonebench	Task success	Not Reported
Kernel-Smith: A Unified Recipe for Evolutionary Kernel Optimization Mar 30, 2026	Yes Critique Edit	Not Reported	Kernelbench	Not Reported	Not Reported
RuleForge: Automated Generation and Validation for Web Vulnerability Detection at Scale Apr 2, 2026	Yes Expert Verification	Llm As Judge , Automatic Metrics	Not Reported	Auroc	Not Reported
ReDAct: Uncertainty-Aware Deferral for LLM Agents Apr 8, 2026	No Not Reported	Simulation Env	ALFWorld	Token cost	Not Reported
LUDOBENCH: Evaluating LLM Behavioural Decision-Making Through Spot-Based Board Game Scenarios in Ludo Apr 7, 2026	No Not Reported	Simulation Env	Ludobench	Dice	Not Reported
QED-Nano: Teaching a Tiny Model to Prove Hard Theorems Apr 6, 2026	Yes Rubric Rating	Automatic Metrics	Not Reported	Inference cost	Not Reported
MemRerank: Preference Memory for Personalized Product Reranking Mar 31, 2026	Yes Pairwise Preference	Automatic Metrics	Not Reported	Accuracy , Relevance	Not Reported
FlowForge: A Staged Local Rollout Engine for Flow-Field Prediction Apr 21, 2026	No Not Reported	Automatic Metrics	Pdebench , Cfdbench	Accuracy	Not Reported
S0 Tuning: Zero-Overhead Adaptation of Hybrid Recurrent-Attention Models Apr 1, 2026	No Not Reported	Automatic Metrics	MATH 500 , GSM8K	Pass@1 , Inference cost	Not Reported

Protocol Diff (Top Papers)

Fast side-by-side comparison for the highest-ranked papers in this hub.

Signal	Personalized RewardBench: Evaluating Reward Models…	TraceSafe: A Systematic Assessment of LLM Guardrail…	Paper Reconstruction Evaluation: Evaluating Present…
Human Feedback	Pairwise Preference, Rubric Rating	Red Team	Rubric Rating
Evaluation Modes	Human Eval, Automatic Metrics	Automatic Metrics	Automatic Metrics
Benchmarks	Rewardbench	Tracesafe Bench	Paperwrite Bench
Metrics	Accuracy, Helpfulness	Accuracy	Not reported
Quality Controls	Not reported	Not reported	Not reported
Rater Population	Unknown	Unknown	Unknown
Annotation Unit	Pairwise	Trajectory	Multi Dim Rubric

Research Utility Snapshot

Human Feedback Mix

Demonstrations (5)
Expert Verification (5)
Pairwise Preference (4)
Rubric Rating (3)

Evaluation Modes

Automatic Metrics (71)
Simulation Env (7)
Llm As Judge (4)
Human Eval (1)

Top Benchmarks

MMLU (7)
DROP (6)
GSM8K (3)
LongBench (3)

Top Metrics

Accuracy (70)
Cost (34)
Precision (14)
F1 (12)

Rater Population Mix

Domain Experts (13)

Quality Controls

Calibration (4)

Coverage diagnostics (sample-based): human-feedback 28.3% · benchmarks 18.3% · metrics 36.7% · quality controls 0.0%.

Top Papers

Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization
Qiyao Ma, Dechen Gao, Rui Cai, Boqi Zhao, Hanchu Zhou · Apr 8, 2026 · Citations: 0

Pairwise PreferenceRubric Rating Human EvalAutomatic Metrics

Pluralistic alignment has emerged as a critical frontier in the development of Large Language Models (LLMs), with reward models (RMs) serving as a central mechanism for capturing diverse human values.
TraceSafe: A Systematic Assessment of LLM Guardrails on Multi-Step Tool-Calling Trajectories
Yen-Shan Chen, Sian-Yao Huang, Cheng-Lin Yang, Yun-Nung Chen · Apr 8, 2026 · Citations: 0

Red Team Automatic Metrics Long Horizon

As large language models (LLMs) evolve from static chatbots into autonomous agents, the primary vulnerability surface shifts from final outputs to intermediate execution traces.
RuleForge: Automated Generation and Validation for Web Vulnerability Detection at Scale
Ayush Garg, Sophia Hager, Jacob Montiel, Aditya Tiwari, Michael Gentile · Apr 2, 2026 · Citations: 0

Expert Verification Llm As JudgeAutomatic Metrics

This paper focuses on RuleForge's architecture and operational deployment for CVE-related threat detection, with particular emphasis on our novel LLM-as-a-judge (Large Language Model as judge) confidence validation system and systematic…
Paper Reconstruction Evaluation: Evaluating Presentation and Hallucination in AI-written Papers
Atsuyuki Miyai, Mashiro Toyooka, Zaiying Zhao, Kenta Watanabe, Toshihiko Yamasaki · Apr 1, 2026 · Citations: 0

Rubric Rating Automatic Metrics

We introduce Paper Reconstruction Evaluation (PaperRecon), an evaluation framework in which an overview (overview.md) is created from an existing paper, after which an agent generates a full paper based on the overview and minimal…
ReDAct: Uncertainty-Aware Deferral for LLM Agents
Dzianis Piatrashyn, Nikita Kotelevskii, Kirill Grishchenkov, Nikita Glazkov, Ivan Nasonov · Apr 8, 2026 · Citations: 0

Simulation Env Long Horizon

Recently, LLM-based agents have become increasingly popular across many applications, including complex sequential decision-making problems.
Do Phone-Use Agents Respect Your Privacy?
Zhengyang Tang, Ke Ji, Xidong Wang, Zihan Ye, Xinyuan Wang · Apr 1, 2026 · Citations: 0

Pairwise Preference Automatic Metrics

We study whether phone-use agents respect privacy while completing benign mobile tasks.
LUDOBENCH: Evaluating LLM Behavioural Decision-Making Through Spot-Based Board Game Scenarios in Ludo
Ojas Jain, Dhruv Kumar · Apr 7, 2026 · Citations: 0

Simulation Env Multi Agent

We introduce LudoBench, a benchmark for evaluating LLM strategic reasoning in Ludo, a stochastic multi-agent board game whose dice mechanics, piece capture, safe-square navigation, and home-path progression introduce meaningful planning…
Kernel-Smith: A Unified Recipe for Evolutionary Kernel Optimization
He Du, Qiming Ge, Jiakai Hu, Aijun Yang, Zheng Cai · Mar 30, 2026 · Citations: 0

Critique Edit Long Horizon

We present Kernel-Smith, a framework for high-performance GPU kernel and operator generation that combines a stable evaluation-driven evolutionary agent with an evolution-oriented post-training recipe.
Wiggle and Go! System Identification for Zero-Shot Dynamic Rope Manipulation
Arthur Jakobsson, Abhinav Mahajan, Karthik Pullalarevu, Krishna Suresh, Yunchao Yao · Apr 23, 2026 · Citations: 0

Automatic MetricsSimulation Env Long Horizon

To mitigate this, we present a novel approach that leverages learned simulation priors to inform goal-conditioned dynamic manipulation of ropes for efficient and accurate task execution.
QED-Nano: Teaching a Tiny Model to Prove Hard Theorems
LM-Provers, Yuxiao Qu, Amrith Setlur, Jasper Dekoninck, Edward Beeching · Apr 6, 2026 · Citations: 0

Rubric Rating Automatic Metrics

To support further research on open mathematical reasoning, we release the full QED-Nano pipeline, including the QED-Nano and QED-Nano-SFT models, the FineProofs-SFT and FineProofs-RL datasets, and the training and evaluation code.
MemRerank: Preference Memory for Personalized Product Reranking
Zhiyuan Peng, Xuyang Wu, Huaixiao Tou, Yi Fang, Yu Gong · Mar 31, 2026 · Citations: 0

Pairwise Preference Automatic Metrics

LLM-based shopping agents increasingly rely on long purchase histories and multi-turn interactions for personalization, yet naively appending raw history to prompts is often ineffective due to noise, length, and relevance mismatch.
Large Language Model Post-Training: A Unified View of Off-Policy and On-Policy Learning
Shiwan Zhao, Zhihu Wang, Xuyang Zhao, Jiaming Zhou, Caiyue Xu · Apr 9, 2026 · Citations: 0

Pairwise Preference Long Horizon

Recent progress spans supervised fine-tuning (SFT), preference optimization, reinforcement learning (RL), process supervision, verifier-guided methods, distillation, and multi-stage pipelines.
FlowForge: A Staged Local Rollout Engine for Flow-Field Prediction
Xiaowen Zhang, Ziming Zhou, Fengnian Zhao, David L. S. Hung · Apr 21, 2026 · Citations: 0

Automatic Metrics Long Horizon

We introduce FlowForge, a staged local rollout engine that predicts future flow fields by compiling a locality-preserving update schedule and executing it with a shared lightweight local predictor.
S0 Tuning: Zero-Overhead Adaptation of Hybrid Recurrent-Attention Models
Jack Young · Apr 1, 2026 · Citations: 0

Automatic Metrics Long Horizon

Using roughly 48 execution-verified HumanEval training solutions, tuning a single initial state matrix per recurrent layer, with zero inference overhead, outperforms LoRA by +10.8 pp (p < 0.001) on HumanEval.
Training-Free Dynamic Upcycling of Expert Language Models
Eros Fanì, Oğuzhan Ersoy · Mar 31, 2026 · Citations: 0

Expert Verification

To address these issues, we introduce Dynamic Upcycling MoE (DUME), a novel approach that reuses dense experts trained on different domains to construct a unified MoE model.
ActionParty: Multi-Subject Action Binding in Generative Video Games
Alexander Pondaven, Ziyi Wu, Igor Gilitschenski, Philip Torr, Sergey Tulyakov · Apr 2, 2026 · Citations: 0

Automatic MetricsSimulation Env Multi Agent

However, these models are largely restricted to single-agent settings, failing to control multiple agents simultaneously in a scene.
Reliable Self-Harm Risk Screening via Adaptive Multi-Agent LLM Systems
Meghana Karnam, Ananya Joshi · Apr 24, 2026 · Citations: 0

Llm As Judge Long Horizon

Emerging AI systems in behavioral health and psychiatry use multi-step or multi-agent LLM pipelines for tasks like assessing self-harm risk and screening for depression.
Weakly Supervised Distillation of Hallucination Signals into Transformer Representations
Shoaib Sadiq Salehmohamed, Jinal Prashant Thakkar, Hansika Aredla, Shaik Mohammed Omar, Shalmali Ayachit · Apr 7, 2026 · Citations: 0

Llm As JudgeAutomatic Metrics

We introduce a weak supervision framework that combines three complementary grounding signals: substring matching, sentence embedding similarity, and an LLM as a judge verdict to label generated responses as grounded or hallucinated without…
SkillX: Automatically Constructing Skill Knowledge Bases for Agents
Chenxi Wang, Zhuoyun Yu, Xin Xie, Wuguannan Yao, Runnan Fang · Apr 6, 2026 · Citations: 0

Automatic Metrics Long Horizon

Learning from experience is critical for building capable large language model (LLM) agents, yet prevailing self-evolving paradigms remain inefficient: agents learn in isolation, repeatedly rediscover similar behaviors from limited…
SHAPE: Stage-aware Hierarchical Advantage via Potential Estimation for LLM Reasoning
Zhengyang Ai, Zikang Shan, Xiaodong Ai, Jingxian Tang, Hangkai Hu · Apr 8, 2026 · Citations: 0

Automatic Metrics Long Horizon

Extensive experiments in math reasoning across three base models and five benchmarks demonstrate that SHAPE achieves an average accuracy gain of 3% with 30% reduced token consumption.
Unifying Group-Relative and Self-Distillation Policy Optimization via Sample Routing
Gengsheng Li, Tianyu Yang, Junfeng Fang, Mingyang Song, Mao Zheng · Apr 2, 2026 · Citations: 0

Automatic Metrics Long Horizon

Evaluated across five benchmarks and two model scales, SRPO achieves both the rapid early improvement of SDPO and the long-horizon stability of GRPO.
LEO: Graph Attention Network based Hybrid Multi Sensor Extended Object Fusion and Tracking for Autonomous Driving Applications
Mayank Mayank, Bharanidhar Duraisamy, Florian Geiss · Apr 2, 2026 · Citations: 0

Automatic Metrics Long Horizon

Evaluations on the Mercedes-Benz DRIVE PILOT SAE L3 dataset demonstrate real-time computational efficiency suitable for production systems; additional validation on public datasets such as View of Delft (VoD) further confirms cross-dataset…
Selecting Decision-Relevant Concepts in Reinforcement Learning
Naveen Raman, Stephanie Milani, Fei Fang · Apr 6, 2026 · Citations: 0

Expert Verification

Training interpretable concept-based policies requires practitioners to manually select which human-understandable concepts an agent should reason with when making sequential decisions.
FourierMoE: Fourier Mixture-of-Experts Adaptation of Large Language Models
Juyong Jiang, Fan Wang, Hong Qi, Sunghun Kim, Jing Tang · Apr 2, 2026 · Citations: 0

Expert Verification

Extensive evaluations across 28 benchmarks, multiple model architectures, and scales demonstrate that FourierMoE consistently outperforms competitive baselines in both single-task and multi-task settings while using significantly fewer…
A Survey of On-Policy Distillation for Large Language Models
Mingyang Song, Mao Zheng · Apr 1, 2026 · Citations: 0

Expert VerificationDemonstrations

We systematically analyze representative methods, examine industrial deployments, and identify open problems including distillation scaling laws, uncertainty-aware feedback, and agent-level distillation.
From High-Dimensional Spaces to Verifiable ODD Coverage for Safety-Critical AI-based Systems
Thomas Stefani, Johann Maximilian Christensen, Elena Hoemann, Frank Köster, Sven Hallerbach · Apr 2, 2026 · Citations: 0

Simulation Env Long Horizon

While Artificial Intelligence (AI) offers transformative potential for operational performance, its deployment in safety-critical domains such as aviation requires strict adherence to rigorous certification standards.
Learning to Play Blackjack: A Curriculum Learning Perspective
Amirreza Alasti, Efe Erdal, Yücel Celik, Theresa Eimer · Mar 31, 2026 · Citations: 0

Automatic MetricsSimulation Env

We propose a novel framework that uses a Large Language Model (LLM) to dynamically generate a curriculum over available actions, enabling the agent to incorporate each action individually.
The Detection-Extraction Gap: Models Know the Answer Before They Can Say It
Hanyang Wang, Mingxuan Zhu · Apr 8, 2026 · Citations: 0

Automatic Metrics Tool Use

Across five model configurations, two families, and three benchmarks, we find that 52--88% of chain-of-thought tokens are produced after the answer is recoverable from a partial prefix.
Learning-augmented robotic automation for real-world manufacturing
Yunho Kim, Quan Nguyen, Taewhan Kim, Youngjin Heo, Joonho Lee · Apr 24, 2026 · Citations: 0

Demonstrations

Here we present Learning-Augmented Robotic Automation, a hybrid system that integrates learned task controllers and a neural 3D safety monitor into conventional industrial workflows.
Preserve Support, Not Correspondence: Dynamic Routing for Offline Reinforcement Learning
Zhancun Mu, Guangyu Zhao, Yiwu Zhong, Chi Zhang · Apr 24, 2026 · Citations: 0

Demonstrations

We propose DROL, a latent-conditioned one-step actor trained with top-1 dynamic routing.
Removing Sandbagging in LLMs by Training with Weak Supervision
Emil Ryd, Henning Bartsch, Julian Stastny, Joe Benton, Vivek Hebbar · Apr 23, 2026 · Citations: 0

Demonstrations

As AI systems begin to automate complex tasks, supervision increasingly relies on weaker models or limited human oversight that cannot fully verify output quality.
Shared Lexical Task Representations Explain Behavioral Variability In LLMs
Zhuonan Yang, Jacob Xiaochen Li, Francisco Piedrahita Velez, Eric Todd, David Bau · Apr 23, 2026 · Citations: 0

Demonstrations

One of the most common complaints about large language models (LLMs) is their prompt sensitivity -- that is, the fact that their ability to perform a task or provide a correct answer to a question can depend unpredictably on the way the…
When Prompts Override Vision: Prompt-Induced Hallucinations in LVLMs
Pegah Khayatan, Jayneel Parekh, Arnaud Dapogny, Mustafa Shukor, Alasdair Newson · Apr 23, 2026 · Citations: 0
TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale
Jun Wang, Ziyin Zhang, Rui Wang, Hang Yu, Peng Di · Apr 23, 2026 · Citations: 0
StructMem: Structured Memory for Long-Horizon Behavior in LLMs
Buqiang Xu, Yijun Chen, Jizhan Fang, Ruobin Zhong, Yunzhi Yao · Apr 23, 2026 · Citations: 0
Fixation Sequences as Time Series: A Topological Approach to Dyslexia Detection
Marius Huber, David R. Reich, Lena A. Jäger · Apr 23, 2026 · Citations: 0
Generalizing Numerical Reasoning in Table Data through Operation Sketches and Self-Supervised Learning
Hanjun Cho, Gahyun Yoo, Hanseong Kim, Jay-Yoon Lee · Apr 23, 2026 · Citations: 0
Cross-Domain Data Selection and Augmentation for Automatic Compliance Detection
Fariz Ikhwantri, Dusica Marijan · Apr 23, 2026 · Citations: 0
mcdok at SemEval-2026 Task 13: Finetuning LLMs for Detection of Machine-Generated Code
Adam Skurla, Dominik Macko, Jakub Simko · Apr 23, 2026 · Citations: 0
Beyond Single Plots: A Benchmark for Question Answering on Multi-Charts
Azher Ahmed Efat, Seok Hwan Song, Wallapak Tavanapong · Apr 23, 2026 · Citations: 0
Sub-Token Routing in LoRA for Adaptation and Query-Aware KV Compression
Wei Jiang, Wei Wang · Apr 23, 2026 · Citations: 0
Ideological Bias in LLMs' Economic Causal Reasoning
Donggyu Lee, Hyeok Yun, Jungwon Kim, Junsik Min, Sungwon Park · Apr 23, 2026 · Citations: 0
Understanding and Mitigating Spurious Signal Amplification in Test-Time Reinforcement Learning for Math Reasoning
Yongcan Yu, Lingxiao He, Jian Liang, Kuangpu Guo, Meng Wang · Apr 23, 2026 · Citations: 0
Explainable Disentangled Representation Learning for Generalizable Authorship Attribution in the Era of Generative AI
Hieu Man, Van-Cuong Pham, Nghia Trung Ngo, Franck Dernoncourt, Thien Huu Nguyen · Apr 23, 2026 · Citations: 0
Cross-Entropy Is Load-Bearing: A Pre-Registered Scope Test of the K-Way Energy Probe on Bidirectional Predictive Coding
Jon-Paul Cacioli · Apr 23, 2026 · Citations: 0
Hyperloop Transformers
Abbas Zeitoun, Lucas Torroba-Hennigen, Yoon Kim · Apr 23, 2026 · Citations: 0
Learning Dynamic Representations and Policies from Multimodal Clinical Time-Series with Informative Missingness
Zihan Liang, Ziwen Pan, Ruoxuan Xiong · Apr 23, 2026 · Citations: 0
Adaptive Instruction Composition for Automated LLM Red-Teaming
Jesse Zymet, Andy Luo, Swapnil Shinde, Sahil Wadhwa, Emily Chen · Apr 22, 2026 · Citations: 0
Slot Machines: How LLMs Keep Track of Multiple Entities
Paul C. Bogdan, Jack Lindsey · Apr 22, 2026 · Citations: 0
Cross-Session Threats in AI Agents: Benchmark, Evaluation, and Algorithms
Ari Azarafrooz · Apr 22, 2026 · Citations: 0
TabSHAP
Aryan Chaudhary, Prateek Agarwal, Tejasvi Alladi · Apr 22, 2026 · Citations: 0
How Much Is One Recurrence Worth? Iso-Depth Scaling Laws for Looped Language Models
Kristian Schwethelm, Daniel Rueckert, Georgios Kaissis · Apr 22, 2026 · Citations: 0
Weighting What Matters: Boosting Sample Efficiency in Medical Report Generation via Token Reweighting
Alexander Weers, Daniel Rueckert, Martin J. Menten · Apr 22, 2026 · Citations: 0
DWTSumm: Discrete Wavelet Transform for Document Summarization
Rana Salama, Abdou Youssef, Mona Diab · Apr 22, 2026 · Citations: 0
Convergent Evolution: How Different Language Models Learn Similar Number Representations
Deqing Fu, Tianyi Zhou, Mikhail Belkin, Vatsal Sharan, Robin Jia · Apr 22, 2026 · Citations: 0
Working Memory Constraints Scaffold Learning in Transformers under Data Scarcity
Pranava Madhyastha, Dagmar Adamcova · Apr 22, 2026 · Citations: 0
COMPASS: COntinual Multilingual PEFT with Adaptive Semantic Sampling
Noah Flynn · Apr 22, 2026 · Citations: 0
Self-Aware Vector Embeddings for Retrieval-Augmented Generation: A Neuroscience-Inspired Framework for Temporal, Confidence-Weighted, and Relational Knowledge
Naizhong Xu · Apr 22, 2026 · Citations: 0
CHASM: Unveiling Covert Advertisements on Chinese Social Media
Jingyi Zheng, Tianyi Hu, Yule Liu, Zhen Sun, Zongmin Zhang · Apr 22, 2026 · Citations: 0
MOMO: A framework for seamless physical, verbal, and graphical robot skill learning and adaptation
Markus Knauer, Edoardo Fiorini, Maximilian Mühlbauer, Stefan Schneyer, Promwat Angsuratanawech · Apr 22, 2026 · Citations: 0

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