STING is an automated red-teaming framework measuring multi-turn illicit assistance in LLM agents. It constructs step-by-step illicit plans grounded in benign personas and uses judge agents to track completion. Multilingual evaluation across six non-English languages shows attack success does not consistently increase in lower-resource languages, diverging from chatbot findings.
SPOT (Surgical Post-Training) is an on-policy distillation framework that injects reasoning capabilities into LLMs while preserving prior knowledge. With 4k rectified math pairs, it improves Qwen3-8B by 6.2% on average in 16 minutes on 8x H800, using KL-constrained reward formulation and minimal-edit error correction pipeline.
CounterRefine adds a lightweight repair layer for RAG: after an initial answer, the system issues answer-conditioned queries to retrieve candidate-specific counterevidence, then applies a deterministically-validated KEEP/REVISE refinement step. On SimpleQA, improves baseline by up to 5.8 correct-rate points; modifies 5.6% of outputs with 180 beneficial changes versus 8 harmful ones.
CoDiLA combines diffusion with local autoregressive decoding for parallel code generation. A compact auxiliary AR model (0.6B parameters) ensures syntactic coherence on diffusion latents, eliminating artifacts while preserving bidirectional generation and sub-linear latency.
Comparative study of interpretability in Mixture-of-Experts (MoE) architectures vs dense networks. MoE experts show lower neuronal polysemanticity than dense FFNs, especially with sparse routing. Experts function as fine-grained linguistic task specialists (e.g., closing LaTeX brackets), not broad domain specialists. Code released.
STEM proposes a framework for Knowledge Graph-based Question Answering (KGQA) that reframes multi-hop reasoning as schema-guided graph search. Uses a Semantic-to-Structural Projection pipeline and Triple-Dependent GNN to generate a Global Guidance Subgraph. Achieves SOTA on multiple multi-hop benchmarks.
Comparative study of fine-tuning vs. in-context learning on LLMs using formal language tasks. Fine-tuning outperforms ICL on in-distribution generalization, but both perform equally on out-of-distribution. Inductive biases diverge at higher proficiency levels. ICL shows sensitivity to vocabulary and model size.
Behavioral evaluation methodology for agentic AI systems: scoring intermediate decisions via LLM judge ensemble across 6 dimensions (regime detection, routing, adaptation, risk calibration, strategy coherence, error recovery). Behavioral score correlates at rho=0.72 with Sharpe ratio. Closed-loop reinforcement (SAC) reduces MAPE from 0.61% to 0.54% on 2017-2025 test set.
New MoLF (Mixture of LoRA and Full) method for LLM adaptation that dynamically routes gradients between full fine-tuning and LoRA at optimizer level. Tested on Gemma-3-1B, Qwen2.5-1.5B/3B across SQL, Medical QA, and counterfactual knowledge tasks. MoLF-Efficient outperforms adaptive LoRA approaches by 20% (Fact) and 9% (Med/SQL). Code open-sourced.
MULTITEXTEDIT is a benchmark of 3,600 instances across 12 typologically diverse languages for evaluating text-in-image editing. Authors introduce a language fidelity (LSF) metric detecting script-level errors (missing diacritics, reversed RTL order). Evaluation of 12 systems reveals pronounced cross-lingual degradation, especially on Hebrew and Arabic.
Softmax-attention transformers can implement preconditioned Richardson iteration for in-context Gaussian kernel regression. Authors construct a single-head transformer with O(log(1/ε)) blocks achieving ε-accurate prediction on prompts of length N, where softmax attention produces a Gaussian-kernel operator and ReLU MLP layers perform local scalar arithmetic.
Retrieval-based approach for multi-label legal annotation: frozen embedding model with k-NN prediction on documents and label descriptions. On Eurlex (100 labels), Qwen-8B achieves Macro-F1 49.12 vs 40.41 (GPT-5.2 zero-shot), reduces compute 20-30×. Eliminates hallucinations (GPT-5.2: 0.12-0.9% out-of-taxonomy labels).
CompactAttention optimizes chunked prefill attention for long-context LLMs using Block-Union KV Selection. The method converts block-sparse masks into GQA-aware per-group KV block tables, eliminating explicit KV compaction. On LLaMA-3.1-8B, it achieves 2.72× speedup at 128K tokens while maintaining accuracy near dense attention (RULER benchmark).
E-PMQ introduces expert-guided post-merge quantization to efficiently deploy multi-expert merged models at low precision. Using source expert weights as calibration targets and merged-weight anchoring, E-PMQ improves 4-bit GPTQ from 65.0% to 73.6% on CLIP-ViT-B/32 (8-task merging) and from 34.8% to 76.7% on CLIP-ViT-L/14 (20-task merging).
RTPurbo transforms LLMs into sparse models in ~100 training steps. The approach exploits three observations: only certain heads require full attention, long-range retrieval uses a 16D subspace, and dynamic top-p selection outperforms fixed top-k. Results: 9.36× prefill speedup at 1M tokens, 2.01× decode speedup, accuracy preserved.
ConsumerSimBench, a benchmark built from 1,553 Chinese social-media topics and 23,122 reaction criteria, evaluates whether LLMs can reconstruct real consumer reaction patterns. Gemini-3.1-Pro achieves only 47.8% coverage of criteria, revealing a major gap between technical performance and socially grounded consumer intuition.
Systematic optimization of real-time diffusion models on Apple M3 Ultra (60-core GPU, 512 GB unified memory). CoreML conversion of SDXS-512 distilled model combined with 3-thread camera pipeline achieves 22.7 FPS at 512×512 resolution. Demonstrates that CUDA optimization insights do not transfer to Apple Silicon's unified memory architecture.
Sign-Muon is a 1-bit distributed optimizer combining majority-vote sign aggregation (signSGD) with Muon's polar-step framework. It reduces communication by 32× in float32 and achieves 92.15% accuracy on CIFAR-10/ResNet-50, with 37% less training time on 4 GPUs.
TopoPrior learns reusable collaboration structures for multi-agent LLM systems. Using a conditional variational graph framework, it captures structural regularities across domains offline, reducing online search cost and inference-time token consumption while remaining compatible with existing topology-evolution backbones.
Study reveals visibility asymmetry in multilingual datasets: 118 languages (59% of 200 most-spoken) have zero catalogued datasets per LRE Map and LDC. Using LLM-assisted citation-mining on Semantic Scholar, authors identify 609 unique datasets across 53 low-visibility languages, 356 openly accessible. Data scarcity is a documentation and discoverability issue, not just production.
UA-StatuteRetrieval: 20-year benchmark on 396M Ukrainian court citations. Co-citation predictability declines 33-47% (Adamic-Adar MRR 0.43→0.29). Non-uniform decay: criminal law stable (~0.40), civil law collapses (0.35→0.15) post-2017 reform. Mid-frequency articles (1K-10K citations) lose 50% predictability. E5-large detects 4.3% semantic drift.
PUMA detects semantic redundancy in reasoning chains to halt inference in large reasoning models before wasting tokens. The framework combines a lightweight redundancy detector with answer-level verification, achieving 26.2% average token reduction across five benchmarks while preserving accuracy and reasoning coherence.
New multilingual ASR approach for Nigerian languages using knowledge distillation. SBPN model (120M and 600M parameters) covers Yoruba, Hausa, Igbo, Nigerian Pidgin, and Nigerian English. Achieves 29% average WER reduction vs monolingual baselines, outperforms SOTA models on Common Voice and Fleurs benchmarks.
SocialMemBench evaluates AI memory systems in multi-party social group settings. The benchmark includes 430 personas, 7,355 conversation turns, and 1,031 QA pairs across 5 social archetypes. Gemini 2.5 Flash reaches 0.721 on small networks; open-source frameworks (Mem0, LangMem, Graphiti, Cognee) plateau at 0.12-0.18, revealing a significant capability gap.
BacktestBench is the first large-scale benchmark for automated quantitative backtesting, containing 18,246 annotated QA pairs across 6 million real market records. AutoBacktest, a multi-agent system, translates natural language strategies into reproducible backtests via a Summarizer, SQL Retriever, and Python Coder. Evaluation on 23 mainstream LLMs.
Asynchronous RAG framework predicting when and what to retrieve using three components (retrieval predictor, context monitor, query generator). Achieves 43.5% end-to-end latency reduction and 62.4% time-to-first-token improvement by exploiting semantic precursors in generation dynamics while maintaining answer quality.
TailedTS is a benchmark of 24.69 billion data points from Wikipedia 2024 page views, designed to test time series forecasting models under heavy-tailed and non-Gaussian conditions. The dataset shows 5% of pages account for 70% of traffic, and introduces a periodicity quantification framework revealing high-traffic pages have weaker periodic structure.
SomaliWeb v1: Somali corpus of 819,322 documents (~303M tokens) with BPE-16K tokenizer and language-identification benchmark. Reveals critical defects in HPLT v2 (17.3% exact duplicates, 56.1% mojibake, 10.7% near-duplicates). Tokenizer 40.2% more efficient than cl100k_base on FLORES-200.
arXiv paper demonstrating that multi-turn conversations in low-resource African languages (Afrikaans, Kiswahili, isiXhosa, isiZulu) bypass safety mechanisms in commercial LLMs. Testing ChatGPT, Claude, DeepSeek, Gemini, and Grok shows jailbreak rates from 52.7% to 83.6% depending on model. Translation quality is the critical success factor.
EPIC is an on-device RAG index construction method optimized for personal AI agents. It reduces indexing memory by 2,404× by focusing on user preferences, improves preference-following accuracy by 20.17 percentage points, and decreases retrieval latency by 33.33×. Memory footprint under 1 MB with 29.35 ms/query latency.
EvoMemBench is a unified benchmark evaluating LLM agent memory along two axes: scope (in-episode vs cross-episode) and content (knowledge vs execution-oriented). Comparison of 15 memory methods: long-context baselines remain highly competitive, retrieval-based methods dominate knowledge-intensive tasks, procedural methods excel at execution-oriented tasks.
Preregistered study comparing Vector RAG and LLM-compiled markdown wiki on 13 questions over 24 papers. Wiki excels at cross-paper synthesis and claim-level citation accuracy, but uses more query tokens. A decomposition-based RAG variant recovers most wiki advantages at lower LLM-token cost.
DiSP, a demonstration selection framework for in-context learning, predicts whether a query-context pair will succeed rather than searching for optimal context. On 5 classification datasets with Llama 3-8B and Qwen 2.5-7B, DiSP improves accuracy by 3.4% and achieves 23× end-to-end speedup.
Investigation of LRM internal representations through probe trajectories. Authors show that continuous evolution of concept probability during reasoning predicts final behavior better than static predictions. Max-pooling achieves 95% AUROC across 4 datasets (safety, mathematics).
LongMINT is a benchmark evaluating agents' memory management in long contexts (up to 1.8M tokens) with multi-target interference. 15.6k QA pairs across 4 domains (state tracking, dialogue, Wikipedia revisions, GitHub commits). 7 systems tested (long-context LLMs, RAG, agent frameworks) achieve 27.9% average accuracy, bottlenecked by retrieval and memory construction.
Circuit analysis of a backdoor in an 8B model: a 3-word Latin trigger redirects English output to French. The circuit operates in 3 phases via attention heads, propagates through a subspace orthogonal to natural language-identity directions, then converts via MLP. A single serial bottleneck position controls the entire flow.
EnvFactory automates creation of executable environments and synthesis of multi-turn trajectories for Agentic RL training. Using 85 verified environments across 7 domains, the framework generates 2,575 SFT/RL trajectories and improves Qwen3-series models by +15% on BFCLv3, +8.6% on MCP-Atlas, and +6% on conversational benchmarks.
Formal verification framework for LLM pipelines using Lean 4 certificates. Three certificate families (conflict-aware bilattice, embedding sensitivity, Hoare-style agent action) plus two operators (Maximal Certifiable Residue, Compositional Stability) for high-stakes deployments (regulated finance, clinical support, agentic systems). Compiled artifact covers 22 certificate types, 17/46 declarations axiom-free.
MINE (Mechanistically Interpretable Neural Encoding) applies mechanistic interpretability tools to neural encoding models to localize visual features driving voxel-level activity in human visual cortex. Validated via image generation and counterfactual editing: inserting/removing predicted features shifts neural activation as expected.
Study on how representation geometry organization in language models depends on scale. Subspace PGA metric tests alignment of intermediate geometry with unembedding matrix readout. Small models (≤1024) progressively lose organization at late layers during training, while large models (≥2048) preserve it throughout. Scale determines how geometry organizes for prediction.