Papers are scientific research articles published by labs or universities to present new findings in AI. For example, the paper "Attention Is All You Need" (Google, 2017) introduced the Transformer architecture.
RPCL, a training-only framework for multimodal emotion-cause pair extraction, improves pair-confidence robustness. Using margin constraints and contextual corruption, it increases Pair F1 by 2.58–2.83 points on ECF/MECAD/MEC4 without changing inference.
Local de-identification framework for educational dialogues. Two-stage cascade: union proposer (lightweight encoders + deterministic rules) generates PII candidates, then binary Redact/Keep reviewer uses dialogue context and speaker role. Achieves 0.958 macro F1 on math tutoring transcripts, outperforms commercial API (0.706) and local LLM baseline (0.767), runs on single laptop.
Mechanistic analysis of mathematical reasoning in multilingual LLMs. Math-associated parameters exhibit partial cross-lingual overlap, concentrated in intermediate layers. English produces the largest set of math-relevant parameters, while lower-resource languages reveal smaller parameter sets.
Study of collateral damage in LLM machine unlearning. Authors show damage propagates beyond the forget set following semantic distance gradients, and propose PreUnlearn, a pre-unlearning prediction method to audit risks before execution.
Novel SCPO algorithm for training reward models that balance diverse cultural preferences across subcommunities. Achieves 7-point improvements for minority reward models on PRISM and GlobalOpinionQA (7 countries), with 280% better training data efficiency than full-finetuning.
Morpheus is a morphology-aware neural tokenizer for agglutinative Turkish. The model uses differentiable Poisson-binomial dynamic programming to segment morphemes with 1.425 bits-per-character compression and MorphScore macro-F1 of 0.61 (vs ~0.32 for subword tokenizers). Lossless by construction: decode(encode(w)) = w.
Memorization suppression method in LLMs via output vector editing of MLP neurons. Tested on 4 models (360M-7B parameters), achieves 87.9% suppression on OLMo-7B with 6831 memorized sequences. Complementary approach to existing neuron ablation methods.
RedactionBench is a manually annotated benchmark of 200 documents across 11 domains for evaluating PII redaction in context. Introduced with R-Score, a character-level metric, it shows 35 models (NER, SLM, frontier models) fail on contextual redactions: human consensus 89.4% for mandatory redactions, 47.7% for contextual ones.
Study on evaluating AI-generated radiology reports. Researchers show existing LLMs over-penalize harmless rephrasings while detecting clinical errors. They train lightweight metrics on Qwen3-8B and MedGemma-4B outperforming 32B medical models, with dataset and metric release planned.
ScholarSum introduces a hierarchical knowledge graph framework for abstractive scientific summarization. The system organizes documents into semantically coherent units, generates an initial draft, then refines it through iterative verification and rewriting to ensure logical coherence and factual faithfulness.
New approach combining diffusion and CRF for sequence labelling in NLP. Method conditions a CRF on the full label sequence (noisy), bypassing span limitations of standard CRFs. Results: 16.5% error reduction on POS-tagging.
Gaussian Mixture Attention (GMA) replaces standard attention with probabilistic routing through K learned Gaussian mixture components. Queries and keys map to responsibility vectors in a shared latent space. GMA avoids explicit N×N matrix materialization, reducing memory complexity to O(NK) instead of O(N²). Competitive on long-context classification, but behind SDPA and Mamba on WikiText-103.
Artemis is a causal framework for graph neural networks addressing demographic confounders (age, sex) in multimodal brain imaging (fMRI + DTI). The method applies causal interventions at each brain region independently to learn invariant representations. Tested on ADNI, OASIS, and HCP benchmarks, it improves disease diagnosis and classification tasks.
New geometric complexity measure called Fisher width, a Fisher-geometric analogue of Gaussian width on statistical manifolds. Replaces Euclidean geometry with Fisher information metric to capture local statistical curvature. Develops foundational theory with generalization bounds and computable estimators, validated on MNIST.
SAGE is a post-hoc method to improve selective unlearning in LLMs. It corrects final update vectors by suppressing components damaging retention, without rerunning the original unlearning pipeline. Tested across multiple methods and scales, SAGE reduces the forget-retain trade-off.
Ghost Attractor Networks introduce an efficient dynamical decoder for sequential generation in robotics. With 2.3M parameters, it matches the offline accuracy of a 1.07B-parameter Diffusion Transformer (462× fewer parameters, 32× lower latency). On LIBERO-10, phase conditioning improves success rate by 13.5 percentage points over MLP baseline.
Survey of AI-based models for soil moisture estimation and classification. Five categories compared: statistical time-series, geostatistical methods, classical ML, deep learning, and Bayesian approaches. Data-driven methods provide flexible alternatives to computationally expensive physics-based models.
SWave is a complex-valued recurrent language model (169M parameters) trained on FineWeb-Edu. The paper documents its evolution across three phases, identifying structural failures (cos-domination collapse) and validating critical components (ComplexNorm, Wave Propagation Scan). Final PPL: 22.0 at step 89,861.
Study on grokking (delayed transition from memorization to generalization). Authors show weight norm doesn't directly control grokking delay but acts through logit scale. Fixing norm and varying output temperature, they recover 85% of delay by matching logit scale. Effect is loss-dependent (cross-entropy vs MSE). Logit scale and softmax saturation are the proximal variables.
QAQL framework couples quantum annealing with Q-learning for remaining useful life (RUL) prediction in predictive maintenance. Each Q-value update encoded as QUBO solved on D-Wave Advantage system. Validated on NASA C-MAPSS and fleet maintenance datasets: statistically significant improvements over classical and quantum baselines.
Theoretical paper on memory requirements for generalist agents. Proves that agents performing near-optimally across multiple domains must maintain distinct memory distributions at observational bottlenecks. Memory enables domain disambiguation, transition-model reconstruction, and planning.
WorldLines is a long-horizon embodied agent benchmark testing memory in dynamic household environments. The dataset includes temporally extended traces with dialogues, actions, and object/device state changes. ObsMem, an observer-grounded memory framework, maintains visibility-aware memories and action-native state trails for state-informed decisions.
BeliefDiffusion combines diffusion models and Model Predictive Control for navigation in partially observable environments. The framework generates multimodal belief distributions and plans efficient navigation strategies. Experiments on synthetic maps: outperforms RL and other generative approaches in success rate and path efficiency.
ThinkDeception introduces a progressive reinforcement learning framework for interpretable multimodal deception detection. Using MLLMs, it converts binary classification into explicit reasoning via Chain of Thought. VAC-GRPO with curriculum learning stratified into 4 difficulty tiers achieves SOTA on mainstream benchmarks.
Study combining deep neural networks with XAI (SHAP, SSHAP) to analyse 39 European electricity bidding zones. Identifies solar energy as disproportionate price driver, gas prices as dominant factor, and interconnections revealing interdependence of electricity markets.
New formal theory (HACD-H) modeling emergence of social intelligence in long-term human-AI interaction. Unified framework integrating emotional adaptation, social memory, and personality consistency. Study on 14,700 conversation turns reveals negative correlation between social intelligence and social cognitive energy (r=-0.391, p<0.001), with developmental phase-transition patterns.
Safety Reflection Pretraining inserts short safety reflections into pretraining corpora to establish self-monitoring directly in language modeling. On 1.7B models pretrained on FineWeb-Edu, the method improves safety classification accuracy and substantially reduces success rates of inference-stage and finetuning attacks.
NeSyCat Torch unifies neurosymbolic semantics (classical, fuzzy, probabilistic, neural) under a single truth definition parametrized by monads. Implemented in PyTorch, JAX, and HaskTorch, the framework interprets computational symbols via neural networks. On MNIST addition, outperforms LTN and DeepProbLog in speed and accuracy.
CoreMem introduces a memory architecture for personalized dialogue agents on edge devices (8 GB VRAM). Replaces cosine similarity with Fisher-Rao metric for retrieval and uses Fisher-guided token distillation for compression. Achieves +4.51 pp gains in open-domain reasoning and +4.17 pp in temporal reasoning on LOCOMO and LongMemEval-S benchmarks.
Paper presents speech-driven approach for Chinese dialect discrimination. Combines MFCC features, HMM-DNN speech recognition model, attention mechanism and CNN. Evaluation on two benchmark Chinese dialect corpora shows improvement over state-of-the-art methods.
RegMix-D extends RegMix by leveraging full loss trajectories from proxy runs, not just endpoint losses, to predict optimal data mixtures at multiple training stages. Tested on 25B tokens of Pile with a 1B model, RegMix-D outperforms RegMix and DoReMi across 13 downstream tasks while using 75% less proxy compute.
Study evaluating 42 LLMs (proprietary and open-source) on their ability to measure item discrimination in reading comprehension. Models fail: Spearman correlation of 0.152 in direct prediction, 0.241 in CTT calibration. LLMs do not reliably capture how assessment items distinguish students of different proficiency levels.
ImpSH, a triplet-based framework, improves implicit hate speech detection by aligning posts with implied statements and using context-bounded semi-hard negatives. Evaluated on IHC, SBIC, and DynaHate with BERT and HateBERT, it enhances cross-domain performance and provides more stable representations than standard supervised contrastive approaches.
Distillation framework with synthetic data for financial sentiment analysis. Knowledge transfer from large instruction-tuned teacher to compact student models. Clustering-based seed selection generates synthetic examples via few-shot prompting. Compact model outperforms teacher on complex/noisy text with minimal supervision.
CodeBlock is a structure-aware sparse supervision framework for code LLM fine-tuning. It selects syntactically coherent code blocks rather than isolated tokens, estimating utility via generalized cross-entropy and data-flow signals. On 6 code-generation benchmarks, CodeBlock outperforms full-token SFT while using only 1.9% of supervised response tokens.
Mathematical link established between shock-wave theory and symmetry-quotiented stochastic gradient descent dynamics for neural networks. After quotienting parameter symmetries and entropy coarse-graining, effective dynamics satisfy a viscous Hamilton-Jacobi equation. Applied to MLPs, CNNs, Transformers, and mean-field networks.
DRIFT refines SFT training data distribution using on-policy Influence Functions. The method uses model rollouts as validation targets to minimize proximity gap and debias gradient norm bias. Experiments on 7B instruction and reasoning models show consistent performance ceiling improvements over existing curation baselines.
RGNet, a neural network architecture based on the renormalization group, addresses class imbalance and multidimensional noise for fault diagnosis. The model hierarchically compresses feature space and captures both local details and global patterns. Tested on imbalanced AI4I dataset.
ThousandWorlds is an ML benchmark for climate emulation of potentially habitable exoplanets. The dataset contains ~1800 simulations from 5 global climate models mapping 8 planetary parameters to 3D atmospheric fields. Three nested subsets and two evaluation protocols test 7 baselines; GP-based methods outperform standard deep learning.
Study of functional redundancy in single-layer tanh RNNs using ordered real Schur coordinates. Authors identify nonnormal couplings removable with minimal loss on specific tasks (copy, flip-flop, sine generation), revealing task-dependent approximate functional invariances rather than universal weight-space symmetries.