Reference papers
127 papers on uncertainty estimation, misclassification / failure detection, OOD detection and internal-state probing, 2020–2026 (a few older iconic ones). PDFs are not committed; bash reference_papers/fetch.sh re-downloads them from manifest.tsv. Layout: reference_papers/<venue>/<year>_<slug>.pdf; Findings, demo and workshop papers live under their parent conference with a findings_/demo_/blackboxnlp_ prefix. LaTeX templates for the target venues are under templates/.
NAACL (19)
| year | file | title | source |
|---|---|---|---|
| 2021 | 2021_noisy_ner_confidence.pdf |
Noisy-Labeled NER with Confidence Estimation | acl:2021.naacl-main.269 |
| 2022 | 2022_findings_pcee_bert_early_exit.pdf |
PCEE-BERT: Accelerating BERT Inference via Patient and Confident Early Exiting | acl:2022.findings-naacl.25 |
| 2022 | 2022_wu_revisit_overconfidence_ood.pdf |
Revisit Overconfidence for OOD Detection: Reassigned Contrastive Learning with Adaptive Class-dependent Threshold | acl:2022.naacl-main.307 |
| 2024 | 2024_findings_he_ue_sequential_labeling.pdf |
Uncertainty Estimation on Sequential Labeling via Uncertainty Transmission | acl:2024.findings-naacl.180 |
| 2024 | 2024_geng_survey_confidence_calibration_llm.pdf |
A Survey of Confidence Estimation and Calibration in Large Language Models | acl:2024.naacl-long.366 |
| 2024 | 2024_kabra_program_aided_know.pdf |
Program-Aided Reasoners (Better) Know What They Know | acl:2024.naacl-long.125 |
| 2024 | 2024_ling_uq_icl.pdf |
Uncertainty Quantification for In-Context Learning of Large Language Models | acl:2024.naacl-long.184 |
| 2024 | 2024_zhang_calibration_icl.pdf |
A Study on the Calibration of In-context Learning | acl:2024.naacl-long.340 |
| 2025 | 2025_belief_tree_hallucination.pdf |
A Probabilistic Framework for LLM Hallucination Detection via Belief Tree Propagation | acl:2025.naacl-long.158 |
| 2025 | 2025_beyond_logit_lens_hallucination.pdf |
Beyond Logit Lens: Contextual Embeddings for Robust Hallucination Detection & Grounding in VLMs | acl:2025.naacl-long.488 |
| 2025 | 2025_findings_llm_ood_survey.pdf |
Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey | acl:2025.findings-naacl.333 |
| 2025 | 2025_findings_long_context_hallucination.pdf |
Towards Long Context Hallucination Detection | acl:2025.findings-naacl.436 |
| 2025 | 2025_findings_maqa_data_uncertainty.pdf |
MAQA: Evaluating Uncertainty Quantification in LLMs Regarding Data Uncertainty | acl:2025.findings-naacl.325 |
| 2025 | 2025_findings_sycophancy_ue.pdf |
Accounting for Sycophancy in Language Model Uncertainty Estimation | acl:2025.findings-naacl.438 |
| 2025 | 2025_findings_trainable_scoring_ue.pdf |
Do Not Design, Learn: A Trainable Scoring Function for Uncertainty Estimation in Generative LLMs | acl:2025.findings-naacl.41 |
| 2025 | 2025_findings_vazhentsev_knn_ue.pdf |
Efficient Nearest Neighbor based Uncertainty Estimation for NLP Tasks | acl:2025.findings-naacl.246 |
| 2025 | 2025_mice_model_internal_confidence.pdf |
MICE for CATs: Model-Internal Confidence Estimation for Calibrating Agents with Tools | acl:2025.naacl-long.615 |
| 2025 | 2025_sciurus_uncertainty_circuits.pdf |
SCIURus: Shared Circuits for Interpretable Uncertainty Representations in Language Models | acl:2025.naacl-long.618 |
| 2025 | 2025_vazhentsev_token_density_uq.pdf |
Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of LLMs | acl:2025.naacl-long.113 |
ACL (30)
| year | file | title | source |
|---|---|---|---|
| 2020 | 2020_hendrycks_pretrained_ood_robustness.pdf |
Pretrained Transformers Improve Out-of-Distribution Robustness | acl:2020.acl-main.244 |
| 2020 | 2020_kamath_selective_qa.pdf |
Selective Question Answering under Domain Shift | acl:2020.acl-main.503 |
| 2021 | 2021_xin_art_of_abstention.pdf |
The Art of Abstention: Selective Prediction and Error Regularization for NLP | acl:2021.acl-long.84 |
| 2021 | 2021_xu_unsupervised_ood_pretrained.pdf |
Unsupervised Out-of-Domain Detection via Pre-trained Transformers | acl:2021.acl-long.85 |
| 2022 | 2022_findings_varshney_selective_prediction.pdf |
Investigating Selective Prediction Approaches Across Several Tasks in IID, OOD, and Adversarial Settings | acl:2022.findings-acl.158 |
| 2022 | 2022_park_calibration_mixup_aum.pdf |
On the Calibration of Pre-trained Language Models using Mixup Guided by Area Under the Margin | acl:2022.acl-long.368 |
| 2022 | 2022_vazhentsev_ue_misclassification.pdf |
Uncertainty Estimation of Transformer Predictions for Misclassification Detection | acl:2022.acl-long.566 |
| 2022 | 2022_zhou_knn_contrastive_ood.pdf |
KNN-Contrastive Learning for Out-of-Domain Intent Classification | acl:2022.acl-long.352 |
| 2023 | 2023_chen_close_look_calibration_plm.pdf |
A Close Look into the Calibration of Pre-trained Language Models | acl:2023.acl-long.75 |
| 2023 | 2023_chen_evaluation_selective_prediction.pdf |
On the Evaluation of Neural Selective Prediction Methods for NLP | acl:2023.acl-long.437 |
| 2023 | 2023_findings_pseudo_outlier_exposure.pdf |
Pseudo Outlier Exposure for Out-of-Distribution Detection using Pretrained Transformers | acl:2023.findings-acl.95 |
| 2023 | 2023_findings_yin_llms_know_dont_know.pdf |
Do Large Language Models Know What They Don't Know? | acl:2023.findings-acl.551 |
| 2023 | 2023_uppaal_is_finetuning_needed.pdf |
Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain Detection | acl:2023.acl-long.717 |
| 2023 | 2023_vazhentsev_hybrid_uq.pdf |
Hybrid Uncertainty Quantification for Selective Text Classification in Ambiguous Tasks | acl:2023.acl-long.652 |
| 2024 | 2024_duan_shifting_attention_relevance.pdf |
Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form LLMs | acl:2024.acl-long.276 |
| 2024 | 2024_findings_fadeeva_token_uq_factcheck.pdf |
Fact-Checking the Output of LLMs via Token-Level Uncertainty Quantification | acl:2024.findings-acl.558 |
| 2024 | 2024_findings_su_mind_internal_states.pdf |
Unsupervised Real-Time Hallucination Detection based on the Internal States of LLMs | acl:2024.findings-acl.854 |
| 2024 | 2024_mars_meaning_aware_scoring.pdf |
MARS: Meaning-Aware Response Scoring for Uncertainty Estimation in Generative LLMs | acl:2024.acl-long.419 |
| 2025 | 2025_findings_survey_ue_llm.pdf |
A Survey of Uncertainty Estimation Methods on Large Language Models | acl:2025.findings-acl.1101 |
| 2025 | 2025_hidden_states_hiding_something.pdf |
Are the Hidden States Hiding Something? Testing the Limits of Factuality-Encoding Capabilities in LLMs | acl:2025.acl-long.304 |
| 2025 | 2025_icr_probe_hidden_state_dynamics.pdf |
ICR Probe: Tracking Hidden State Dynamics for Reliable Hallucination Detection in LLMs | acl:2025.acl-long.880 |
| 2025 | 2025_internal_states_knowledge_boundary.pdf |
Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception | acl:2025.acl-long.1184 |
| 2025 | 2025_overconfident_and_other_lies.pdf |
Your Model is Overconfident, and Other Lies We Tell Ourselves | acl:2025.acl-long.269 |
| 2025 | 2025_prompt_guided_internal_states.pdf |
Prompt-Guided Internal States for Hallucination Detection of Large Language Models | acl:2025.acl-long.1058 |
| 2025 | 2025_short_uq_evaluation_spurious.pdf |
Revisiting Uncertainty Quantification Evaluation in Language Models: Spurious Interactions with Response Length | acl:2025.acl-short.60 |
| 2025 | 2025_zhang_llm_ue_in_the_wild.pdf |
Reconsidering LLM Uncertainty Estimation Methods in the Wild | acl:2025.acl-long.1429 |
| 2026 | 2026_findings_confidence_evaluation_pitfalls.pdf |
Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification | acl:2026.findings-acl.1671 |
| 2026 | 2026_findings_internal_states_recall.pdf |
Do LLMs Really Know What They Don't Know? Internal States Mainly Reflect Knowledge Recall | acl:2026.findings-acl.34 |
| 2026 | 2026_reprobe_internal_states.pdf |
ReProbe: Efficient Test-Time Scaling of Multi-Step Reasoning by Probing Internal States | acl:2026.acl-long.536 |
| 2026 | 2026_topological_divergence_attention_hallucination.pdf |
Hallucination Detection in LLMs with Topological Divergence on Attention Graphs | acl:2026.acl-long.704 |
EMNLP (25)
| year | file | title | source |
|---|---|---|---|
| 2020 | 2020_desai_calibration_pretrained_transformers.pdf |
Calibration of Pre-trained Transformers | acl:2020.emnlp-main.21 |
| 2020 | 2020_he_accurate_ue_text_classification.pdf |
Towards More Accurate Uncertainty Estimation In Text Classification | acl:2020.emnlp-main.671 |
| 2020 | 2020_kong_calibrated_finetuning.pdf |
Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data | acl:2020.emnlp-main.102 |
| 2021 | 2021_arora_types_of_ood_texts.pdf |
Types of Out-of-Distribution Texts and How to Detect Them | acl:2021.emnlp-main.835 |
| 2021 | 2021_kushnareva_topology_attention.pdf |
Artificial Text Detection via Examining the Topology of Attention Maps | acl:2021.emnlp-main.50 |
| 2021 | 2021_zhou_contrastive_ood.pdf |
Contrastive Out-of-Distribution Detection for Pretrained Transformers | acl:2021.emnlp-main.84 |
| 2022 | 2022_findings_cherniavskii_acceptability_topology.pdf |
Acceptability Judgements via Examining the Topology of Attention Maps | acl:2022.findings-emnlp.7 |
| 2022 | 2022_findings_cho_implicit_layer_ensemble_ood.pdf |
Enhancing OOD Detection in NLU via Implicit Layer Ensemble | acl:2022.findings-emnlp.55 |
| 2022 | 2022_findings_holistic_sentence_embeddings_ood.pdf |
Holistic Sentence Embeddings for Better Out-of-Distribution Detection | acl:2022.findings-emnlp.497 |
| 2022 | 2022_findings_ulmer_uncertainty_calibration_nlp.pdf |
Exploring Predictive Uncertainty and Calibration in NLP | acl:2022.findings-emnlp.198 |
| 2022 | 2022_findings_xiao_uq_plm_empirical.pdf |
Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis | acl:2022.findings-emnlp.538 |
| 2023 | 2023_demo_lm_polygraph.pdf |
LM-Polygraph: Uncertainty Estimation for Language Models | acl:2023.emnlp-demo.41 |
| 2023 | 2023_findings_azaria_internal_state_lying.pdf |
The Internal State of an LLM Knows When It's Lying | acl:2023.findings-emnlp.68 |
| 2023 | 2023_findings_document_ood_critical.pdf |
A Critical Analysis of Document Out-of-Distribution Detection | acl:2023.findings-emnlp.332 |
| 2023 | 2023_flats_likelihood_ratio_ood.pdf |
FLatS: Principled Out-of-Distribution Detection with Feature-Based Likelihood Ratio Score | acl:2023.emnlp-main.554 |
| 2023 | 2023_slobodkin_unanswerability_hidden_states.pdf |
The Curious Case of Hallucinatory (Un)answerability: Finding Truths in the Hidden States of LLMs | acl:2023.emnlp-main.220 |
| 2023 | 2023_tian_just_ask_for_calibration.pdf |
Just Ask for Calibration | acl:2023.emnlp-main.330 |
| 2024 | 2024_blackboxnlp_ji_internal_states_risk.pdf |
LLM Internal States Reveal Hallucination Risk Faced With a Query | acl:2024.blackboxnlp-1.6 |
| 2024 | 2024_embedding_gradient_say_wrong.pdf |
Embedding and Gradient Say Wrong: A White-Box Method for Hallucination Detection | acl:2024.emnlp-main.116 |
| 2024 | 2024_findings_internal_inspector.pdf |
InternalInspector: Robust Confidence Estimation in LLMs through Internal States | acl:2024.findings-emnlp.751 |
| 2024 | 2024_gottesman_geva_knowledge_without_token.pdf |
Estimating Knowledge in LLMs Without Generating a Single Token | acl:2024.emnlp-main.232 |
| 2025 | 2025_findings_representation_detectors_fail_ood.pdf |
Representation-based Broad Hallucination Detectors Fail to Generalize Out of Distribution | acl:2025.findings-emnlp.952 |
| 2025 | 2025_illusion_of_progress_hallucination.pdf |
The Illusion of Progress: Re-evaluating Hallucination Detection in LLMs | acl:2025.emnlp-main.1761 |
| 2025 | 2025_kim_layerwise_information_deficiency.pdf |
Detecting LLM Hallucination Through Layer-wise Information Deficiency | acl:2025.emnlp-main.1644 |
| 2025 | 2025_uq_heads_predict_question.pdf |
A Head to Predict and a Head to Question: Pre-trained Uncertainty Quantification Heads | acl:2025.emnlp-main.1809 |
TACL (1)
| year | file | title | source |
|---|---|---|---|
| 2025 | 2025_vashurin_lm_polygraph_benchmark.pdf |
Benchmarking Uncertainty Quantification Methods for LLMs with LM-Polygraph | acl:2025.tacl-1.11 |
EACL (2)
| year | file | title | source |
|---|---|---|---|
| 2024 | 2024_spuq_perturbation_uq.pdf |
SPUQ: Perturbation-Based Uncertainty Quantification for Large Language Models | acl:2024.eacl-long.143 |
| 2026 | 2026_confidence_estimators_reasoning_models.pdf |
How Reliable are Confidence Estimators for Large Reasoning Models? | acl:2026.eacl-long.78 |
COLING (1)
| year | file | title | source |
|---|---|---|---|
| 2020 | 2020_mahalanobis_generative_ood.pdf |
A Deep Generative Distance-Based Classifier for Out-of-Domain Detection with Mahalanobis Space | acl:2020.coling-main.125 |
ICLR (12)
| year | file | title | source |
|---|---|---|---|
| 2021 | 2021_sehwag_ssd.pdf |
SSD: A Unified Framework for Self-Supervised Outlier Detection | arxiv:2103.12051 |
| 2023 | 2023_burns_discovering_latent_knowledge.pdf |
Discovering Latent Knowledge in Language Models Without Supervision | arxiv:2212.03827 |
| 2023 | 2023_galil_selective_prediction_imagenet.pdf |
What Can We Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers | arxiv:2302.11874 |
| 2023 | 2023_he_preserving_pretrained_features_calibration.pdf |
Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models | arxiv:2305.19249 |
| 2023 | 2023_jaeger_failure_detection_evaluation.pdf |
A Call to Reflect on Evaluation Practices for Failure Detection in Image Classification | arxiv:2211.15259 |
| 2023 | 2023_kuhn_semantic_uncertainty.pdf |
Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in NLG | arxiv:2302.09664 |
| 2023 | 2023_ming_hyperspherical_ood.pdf |
How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection? | arxiv:2203.04450 |
| 2023 | 2023_ren_rmd_selective_generation.pdf |
Out-of-Distribution Detection and Selective Generation for Conditional Language Models | arxiv:2209.15558 |
| 2024 | 2024_chen_inside.pdf |
INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection | arxiv:2402.03744 |
| 2024 | 2024_chuang_dola.pdf |
DoLa: Decoding by Contrasting Layers Improves Factuality in LLMs | arxiv:2309.03883 |
| 2024 | 2024_xiong_llms_express_uncertainty.pdf |
Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation | arxiv:2306.13063 |
| 2025 | 2025_orgad_llms_know_more.pdf |
LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations | arxiv:2410.02707 |
NeurIPS (7)
| year | file | title | source |
|---|---|---|---|
| 2020 | 2020_liu_energy_ood.pdf |
Energy-based Out-of-distribution Detection | arxiv:2010.03759 |
| 2020 | 2020_liu_sngp.pdf |
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness | arxiv:2006.10108 |
| 2021 | 2021_fort_exploring_limits_ood.pdf |
Exploring the Limits of Out-of-Distribution Detection | arxiv:2106.03004 |
| 2022 | 2022_kotelevskii_nuq.pdf |
Nonparametric Uncertainty Quantification for Single Deterministic Neural Network | arxiv:2202.03101 |
| 2023 | 2023_li_inference_time_intervention.pdf |
Inference-Time Intervention: Eliciting Truthful Answers from a Language Model | arxiv:2306.03341 |
| 2023 | 2023_tulchinskii_intrinsic_dimension.pdf |
Intrinsic Dimension Estimation for Robust Detection of AI-Generated Texts | arxiv:2306.04723 |
| 2024 | 2024_traub_augrc.pdf |
Overcoming Common Flaws in the Evaluation of Selective Classification Systems | arxiv:2407.01032 |
ICML (5)
| year | file | title | source |
|---|---|---|---|
| 2020 | 2020_moon_crl.pdf |
Confidence-Aware Learning for Deep Neural Networks | arxiv:2007.01458 |
| 2020 | 2020_vanamersfoort_duq.pdf |
Uncertainty Estimation Using a Single Deep Deterministic Neural Network | arxiv:2003.02037 |
| 2022 | 2022_hendrycks_maxlogit.pdf |
Scaling Out-of-Distribution Detection for Real-World Settings | arxiv:1911.11132 |
| 2022 | 2022_sun_knn_ood.pdf |
Out-of-Distribution Detection with Deep Nearest Neighbors | arxiv:2204.06507 |
| 2025 | 2025_skean_layer_by_layer.pdf |
Layer by Layer: Uncovering Hidden Representations in Language Models | arxiv:2502.02013 |
AAAI (1)
| year | file | title | source |
|---|---|---|---|
| 2021 | 2021_podolskiy_mahalanobis_transformers.pdf |
Revisiting Mahalanobis Distance for Transformer-Based Out-of-Domain Detection | arxiv:2101.03778 |
CVPR (3)
| year | file | title | source |
|---|---|---|---|
| 2022 | 2022_wang_vim.pdf |
ViM: Out-Of-Distribution with Virtual-logit Matching | arxiv:2203.10807 |
| 2023 | 2023_mukhoti_ddu.pdf |
Deep Deterministic Uncertainty: A New Simple Baseline | arxiv:2102.11582 |
| 2023 | 2023_zhu_openmix.pdf |
OpenMix: Exploring Outlier Samples for Misclassification Detection | arxiv:2303.17093 |
TPAMI (1)
| year | file | title | source |
|---|---|---|---|
| 2024 | 2024_zhu_revisiting_confidence_failure_prediction.pdf |
Revisiting Confidence Estimation: Towards Reliable Failure Prediction | arxiv:2403.02886 |
TMLR (4)
| year | file | title | source |
|---|---|---|---|
| 2022 | 2022_rabanser_training_dynamics.pdf |
Selective Prediction via Training Dynamics | arxiv:2205.13532 |
| 2023 | 2023_lahlou_deup.pdf |
DEUP: Direct Epistemic Uncertainty Prediction | arxiv:2102.08501 |
| 2024 | 2024_lang_ood_nlp_survey.pdf |
A Survey on Out-of-Distribution Detection in NLP | arxiv:2305.03236 |
| 2024 | 2024_lin_generating_with_confidence.pdf |
Generating with Confidence: Uncertainty Quantification for Black-box LLMs | arxiv:2305.19187 |
COLM (1)
| year | file | title | source |
|---|---|---|---|
| 2024 | 2024_marks_geometry_of_truth.pdf |
The Geometry of Truth: Emergent Linear Structure in LLM Representations of True/False Datasets | arxiv:2310.06824 |
UAI (1)
| year | file | title | source |
|---|---|---|---|
| 2024 | 2024_cattelan_fix_broken_confidence.pdf |
How to Fix a Broken Confidence Estimator | arxiv:2305.15508 |
KDD (1)
| year | file | title | source |
|---|---|---|---|
| 2024 | 2024_snyder_early_detection_hallucination.pdf |
On Early Detection of Hallucinations in Factual Question Answering | arxiv:2312.14183 |
IJCV (1)
| year | file | title | source |
|---|---|---|---|
| 2024 | 2024_yang_generalized_ood_survey.pdf |
Generalized Out-of-Distribution Detection: A Survey | arxiv:2110.11334 |
arXiv (12)
| year | file | title | source |
|---|---|---|---|
| 2021 | 2021_ren_relative_mahalanobis.pdf |
A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection | arxiv:2106.09022 |
| 2022 | 2022_kadavath_mostly_know.pdf |
Language Models (Mostly) Know What They Know | arxiv:2207.05221 |
| 2023 | 2023_belrose_tuned_lens.pdf |
Eliciting Latent Predictions from Transformers with the Tuned Lens | arxiv:2303.08112 |
| 2024 | 2024_kossen_semantic_entropy_probes.pdf |
Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs | arxiv:2406.15927 |
| 2024 | 2024_liu_supervised_ue_llm.pdf |
Uncertainty Estimation and Quantification for LLMs: A Simple Supervised Approach | arxiv:2404.15993 |
| 2025 | 2025_clue_hidden_state_clustering.pdf |
CLUE: Non-parametric Verification from Experience via Hidden-State Clustering | arxiv:2510.01591 |
| 2025 | 2025_kim_layerwise_inference_dynamics.pdf |
On the Effect of Uncertainty on Layer-wise Inference Dynamics | arxiv:2507.06722 |
| 2025 | 2025_sridhar_clinical_doubt.pdf |
Mapping Clinical Doubt: Locating Linguistic Uncertainty in LLMs | arxiv:2511.22402 |
| 2025 | 2025_suresh_clap_cross_layer_probing.pdf |
Cross-Layer Attention Probing for Fine-Grained Hallucination Detection | arxiv:2509.09700 |
| 2026 | 2026_cho_confidence_manifold.pdf |
The Confidence Manifold: Geometric Structure of Correctness | arxiv:2602.08159 |
| 2026 | 2026_delajara_representation_trajectories.pdf |
Representation Trajectories Matter | arxiv:2607.26565 |
| 2026 | 2026_eusebi_calibrated_uncertainty_trajectories.pdf |
Reading Calibrated Uncertainty from Language Model Trajectories | arxiv:2605.22864 |
Templates
| dir | venue | source |
|---|---|---|
templates/acl-style-files |
ACL / NAACL / EMNLP / EACL (shared ACL style, acl.sty, acl_natbib.bst) |
github.com/acl-org/acl-style-files |
templates/neurips2025 |
NeurIPS 2025 | media.neurips.cc |
templates/iclr2026 |
ICLR 2026 | github.com/ICLR/Master-Template |
templates/icml2025 |
ICML 2025 | media.icml.cc |
templates/aaai2026-quarto/_extensions/aaai2026 |
AAAI-26 (aaai2026.sty, aaai2026.bst; aaai.org blocks direct download, this is a mirror of the kit's style files) |
github.com/Selbosh/aaai2026-quarto |
templates/ieeetran |
IEEE Transactions / IEEE Access (IEEEtran.cls) |
CTAN |