The Archive

“AI creator” accounts like Aitana Lopez will get a new “AI-generated profile” label. | Image: Aitana Lopez Instagram is finally taking steps to address the rise of fake AI-influencer accounts that have gotten harder to spot. It's also renaming the "AI creator" label to "AI-generated profile" to make it clear when a profile features an AI-generated person that's not a real human being. "We've heard that people don't like seeing a profile that seems human, only to find out later that the person featured is AI-generated," said Instagram in a blog post. "They want to know when a profile features ...

Circleback is also introducing new pricing plans starting from $14 per month

Murano: open-source framework for composable mechanistic interpretability pipelines across loading, attribution, intervention, and evaluation stages.

SwarmBench: benchmark for evaluating LLM-based agent orchestration across accuracy, efficiency, cost, and process quality in dynamic multi-agent systems.

Hybrid CNN-Transformer for Antarctic sea ice forecasting using seasonal factorization; domain-specific climate modeling, not AI capability research.

MIOH: fine-grained multi-image object hallucination benchmark for MLLMs, diagnosing how visual complexity triggers factual errors across multi-image reasoning.

PyKEEN-NSX: modular negative sampling framework for knowledge graph embedding; specialized infrastructure for KG link prediction, not core LLM research.

Geometry of Divergence: framework tracking LLM hidden-state trajectories via temporal curvature and variance to diagnose representation drift in multi-turn reasoning.

Retain-free identity unlearning in MLLMs via localized early-decoder MLP intervention, enabling post-deployment privacy removal without retain sets.

Empirical analysis of precomputed memory (KV caches, compressions) degradation in Llama-3.1-8B: assembly failures and rebuild costs nearing full computation.

BiG-SURE: black-box uncertainty estimator for LLMs and VLMs using cross-temperature semantic agreement and bipartite anchor-probe graphs.

Action-chunk contrastive RL: extending CRL to multi-step action sequences yields +31.7% offline, +93.1% online gains across benchmarks.

MolLedger: graph neural network with per-atom attributions for ADME property prediction in drug discovery.

Quantum-inspired Grassmann-Plucker token mixing for post-disaster building damage assessment from satellite imagery.

GMTS: gradient magnitude-based token selection improves RL training for LLM reasoning tasks.

REER-PT: framework injecting reasoning annotations into raw pre-training data to improve LLM training efficiency.

Active learning for visual grounding without text annotations using ambiguity detection in referring image segmentation.

Synthetic data security risk: targeted bias injection into aligned LLMs via benign text exploiting subliminal learning.

TaxCE: automated multi-level taxonomy construction from unstructured text with rigorous evaluation framework.

Continued pre-training adapts LLMs to Swedish journalism domain with novel benchmark across six editorial tasks.

Joint embedding and codebook learning for generative e-commerce retrieval to address cascading training errors.

DiffSAC: diffusion model guides robust estimation sampling for consensus-based computer vision tasks.

PLC-DPO corrects mislabeled preference pairs in DPO training by classifying each pair as clean, flip, or tie using policy-reference margin calibration.

CNN-BiLSTM architecture with Bayesian optimization for battery state-of-health estimation in energy systems.

Mechanistic interpretability reveals how roleplay jailbreaks reverse LLM refusals by decomposing persona, scenario, and task elements.

Eye-tracking patterns during naturalistic passage reading predict English second-language proficiency better than traditional linguistic tasks.

Model checking as oracle enables systematic testing of LLM-based policy explainers for faithfulness to underlying decision environments.

Non-collapsible performance metrics in clinical AI can mask fairness disparities across demographic subgroups.

Turing-20B-A2B, a 20B-parameter MoE model with 2B active per token, optimizes latency for physical AI via quantile routing.

Q-Strata hierarchically allocates mixed-precision bitwidths across MoE LLM experts to optimize model-level quantization objectives.