The key-value (KV) cache in transformer-based language models stores intermediate computations (keys and values) for all previous tokens, enabling efficient autoregressive decoding. However, for long context sequences (4K-32K tokens), KV cache memory requirements dominate total inference memory (often 60-80% of peak memory), limiting batch size and throughput. This study presents a sliding window KV-cache mechanism combined with importance scoring to reduce memory requirements while maintaining generation quality. The approach maintains only the most recent N tokens (sliding window) in the KV cache, discarding older tokens as new ones are generated. We introduce adaptive importance scoring based on attention weights: tokens with high cumulative attention in recent generation steps are retained in cache, while low-importance tokens are discarded. We evaluate on multiple architectures (Llama 2-7B, Mistral 7B, LLaMA-13B) and tasks (long-document summarization, retrieval-augmented generation, long-context question answering). With a 2048-token sliding window covering 2048/4096 = 50% of a 4K context: Perplexity remains within 2-3% of full-context baseline (typically 93-98% recovery), Memory savings reach 45-55% reduction in KV cache size, Throughput improves 1.8-2.1x due to reduced memory bandwidth, Latency per token decreases by 35-42%. For extreme compression (512-token window covering 12.5% of 4K context): Quality degrades more significantly (80-85% perplexity recovery), but memory reduction reaches 75-80%, enabling batch size improvements of 3-4x. The importance scoring mechanism uses recent attention patterns to identify which older tokens remain relevant. Validation shows the method preserves long-range dependencies needed for retrieval-augmented tasks (retrieval precision within 1-2% of full context). This framework enables efficient inference on memory-constrained devices while maintaining reasonable quality for most applications.
Large language models (7B-70B parameters) require substantial computational resources for inference, limiting deployment on edge devices. Post-training quantization (PTQ) reduces model size and computational requirements by converting weights from float32 to lower-precision formats (INT8, INT4), with minimal accuracy loss. However, INT4 quantization presents challenges due to the reduced dynamic range (256 levels vs. 4.3B for float32). This study develops adaptive calibration techniques for INT4 post-training quantization of instruction-tuned language models, addressing distribution shift between calibration and deployment data. We evaluate multiple calibration strategies: (1) Min-Max static calibration (baseline), (2) Percentile-based (99th, 99.5th percentile), (3) Entropy-based calibration (KL divergence minimization), and (4) Mixed-precision quantization (INT4 for weights, INT8 for activations). Testing on Llama 7B, Mistral 7B, and Phi-2 models using standard benchmarks (MMLU 5-shot accuracy, HellaSwag, PIQA) and custom instruction-following tasks. Results show entropy-based calibration achieves 95.2% of full-precision performance on MMLU, compared to 91.8% for naive min-max quantization (3.4% recovery). Mixed-precision approaches recover 96.1% of performance while reducing model size by 4.1x. Quantization degrades performance more on reasoning-heavy tasks than factual knowledge tasks. The adaptive calibration method automatically selects which layers to keep at INT8 vs INT4 based on sensitivity analysis. Implementation uses NVIDIA CUDA kernels for efficient INT4 inference (~2.8x speedup on RTX 4090 vs. float32). This framework enables practical deployment of 7B+ parameter models on consumer GPUs with <5% accuracy loss.
Contamination events in drinking water distribution systems pose acute public health risks. Early detection is critical—typical contamination (chemical, microbial, or physical) travels through distribution networks, potentially affecting thousands within hours. We present a real-time anomaly detection system using multivariate sensor fusion and Isolation Forest algorithms. The system monitors six water quality parameters simultaneously (pH, turbidity, free chlorine, dissolved oxygen, electrical conductivity, temperature) at normal ranges specified by EPA Safe Drinking Water Act regulations. We evaluate three machine learning approaches: Isolation Forest, Local Outlier Factor (LOF), and multivariate Gaussian detection, on synthetic water quality data spanning 30 days with injected contamination events. Isolation Forest achieves 90.4% F1-score and 89.2% recall with <6 hour mean detection latency. The approach is computationally efficient, operational without internet connectivity, and provides explainable anomalies through feature attribution. Field validation on real distribution systems and integration with SCADA alert systems could enable autonomous contamination response, protecting public health and water infrastructure.
Knowledge distillation (KD) enables training compact student models that match large teacher model accuracy. We conduct a systematic empirical study comparing standard KD (Hinton et al., 2015), feature-level matching, attention transfer, and combined approaches. Through experiments on classification tasks with 10x parameter reduction (2M teacher → 200K student), we demonstrate that combined distillation achieves 98.8% of teacher accuracy versus 92.8% without distillation. We analyze the effectiveness of different loss functions, calibration techniques, and architectural constraints. Our results show feature-level KD provides 0.3% additional benefit over standard KD, while attention transfer contributes minor improvements. Combined approaches achieve best results with <2% accuracy degradation. These findings enable practical deployment of efficient models with minimal quality loss, critical for mobile and edge inference.
Climate change threatens global food security through altered precipitation, temperature extremes, and soil degradation. Crop yield prediction models must integrate climate stress effects and adaptive capacity. This study develops a machine learning framework combining climate variables, soil properties, and degradation metrics to predict crop yields under future climate scenarios. We integrate remotely-sensed vegetation indices (NDVI, EVI), soil moisture from satellite data, and in-situ climate observations from 500+ agricultural districts across diverse climates (humid tropical, semi-arid, temperate). Ground-truth yield data from 2010-2024 provides training labels. Our approach uses gradient boosting (XGBoost) with feature engineering: (1) climate stress indices (thermal stress days, water deficit), (2) soil degradation proxies (organic matter decline rate), (3) adaptive capacity indicators (irrigation access, crop diversity). The model predicts yields with R² = 0.74 across diverse regions and crops (maize, wheat, rice, sorghum). Climate stress accounts for 35-45% of yield variance; soil degradation explains 15-25%; management practices (irrigation, fertilization) explain 20-30%. Under RCP 8.5 scenarios (2050), yields decline 15-30% in water-stressed regions (sub-Saharan Africa) without adaptation; high-adaptation pathways (improved varieties, irrigation expansion, conservation agriculture) reduce losses to 5-10%. Temporal analysis reveals increasing climate volatility: coefficient of variation in yields increases 40% from 2010-2024 compared to 1990-2010 baseline. Yield forecasts 2-3 months before harvest using seasonal climate forecasts achieve correlation 0.65 with actual yields, enabling early warning and policy interventions. Our framework explicitly models interaction between climate stress and adaptive capacity, showing that adaptation effectiveness varies by region (higher in temperate areas, lower where resource constraints limit adoption). This work supports climate-informed agricultural planning and early warning systems for food security.
Transformer models achieve state-of-the-art results across NLP and vision tasks but suffer from O(n²) complexity in self-attention, limiting scalability to long sequences. Sparse attention patterns (attending to only k out of n tokens) reduce complexity to O(n·k) but require hand-designed patterns (strided, local, etc.). This work proposes learned sparse attention using differentiable top-k selection, where the model learns which tokens to attend to during training. We implement a differentiable approximation of top-k via Gumbel-softmax relaxation with straight-through estimators, enabling end-to-end learning of sparse patterns. Our method learns attention sparsity patterns that adapt to each input and layer, capturing task-specific dependencies (e.g., long-range connections for language understanding, local patterns for vision). Experiments on BERT-scale models show that learned sparsity achieves 40-60% reduction in attention FLOPs while maintaining <1% accuracy loss on GLUE, SuperGLUE, and SQuAD. Learned patterns are more efficient than hand-designed baselines: strided attention (40% FLOPs reduction), local attention (50% reduction), and fixed random patterns (45% reduction). Learned sparsity achieves 1.3-1.5x speedup on inference hardware (NVIDIA A100). Notably, learned patterns transfer across similar tasks (e.g., pretrained patterns on MNLI transfer to RTE with 90% efficiency). Analysis reveals that learned patterns exhibit interpretable structure: early layers learn local patterns (attending to adjacent tokens), middle layers learn mixed patterns with long-range jumps, and late layers focus on special tokens. The framework generalizes to vision transformers, achieving 35-50% FLOPs reduction on ImageNet-1K while maintaining accuracy. Our approach is compatible with existing efficient techniques like knowledge distillation and quantization, enabling further speedups when combined. This work demonstrates that learned, task-aware sparse attention is both efficient and effective, providing a principled alternative to hand-designed patterns.
Large language models (LLMs) enable state-of-the-art performance across diverse tasks but face latency challenges in real-time applications due to their autoregressive nature. Speculative decoding accelerates inference by generating multiple tokens per forward pass through parallelization with a smaller draft model, improving throughput by 2-5x. However, existing methods fix the draft length a priori, leading to suboptimal performance since different inputs require different draft lengths to balance accuracy and speed. This study proposes adaptive draft length mechanisms for speculative decoding that dynamically adjust the number of draft tokens based on input characteristics. We implement self-calibrating methods that monitor draft acceptance rates and adjust draft length in real-time without retraining. Our approach uses lightweight heuristics: (1) acceptance-rate-based adjustment, (2) input-length adaptive length, and (3) entropy-based confidence scoring for draft-length selection. Experiments on LLaMA-7B and CodeLLaMA-7B show that adaptive draft length improves token throughput by 15-25% over fixed draft length across diverse benchmarks (MMLU, HellaSwag, HumanEval). Particularly, for long-context inputs (>2000 tokens), adaptive methods achieve 1.3-1.8x throughput improvement while maintaining <1% accuracy loss compared to baseline outputs. Our technique requires no additional model training, works with any existing draft model, and is compatible with other speculative decoding variants like Jacobi decoding. We analyze the draft-length distribution across inputs and find that optimal draft lengths vary significantly: short inputs benefit from longer drafts (8-12 tokens), while long contexts prefer shorter drafts (3-5 tokens). Our self-calibration mechanism learns these patterns within 100 inference steps, enabling immediate deployment without offline profiling. The framework generalizes to different model sizes and draft model architectures. This work demonstrates that adaptive inference strategies can provide substantial speedups for speculative decoding without additional computational overhead or model modifications.
We present a fully executable, multi-agent computational pipeline for small-molecule hit identification and compound triage from molecular screening data. Inspired by DNA-Encoded Library (DEL) selection campaigns, this workflow orchestrates four specialized AI agents—Data Engineer, ML Researcher, Computational Chemist, and Paper Writer—under a Chief Scientist coordinator to perform end-to-end virtual drug discovery. Using the MoleculeNet HIV dataset (41,127 compounds, ~3.5% active), our pipeline achieves an AUC-ROC of 0.8095 and an 8.82× enrichment factor in the top-500 predicted actives. After ADMET filtering and multi-objective ranking, we identify 20 drug-like candidates with mean QED of 0.768, mean synthetic accessibility score of 2.83, and 100% Lipinski compliance. Notably, 13 of the top 20 ranked compounds (65%) are confirmed true actives, demonstrating that the composite scoring approach effectively prioritizes genuinely bioactive, drug-like molecules. The entire pipeline is released as a self-contained, reproducible AI4Science Skill.
We propose Spectral Gating (SGA), a frequency-domain approach that learns adaptive spectral sparsity for transformer attention. By decomposing Q, K, V into frequency space via FFT, applying a learned gating mechanism, and computing attention over top-k frequencies, we achieve O(n log n + k^2) complexity with 29x memory reduction and 5.16x speedup at long sequences, while maintaining competitive perplexity (3.2% improvement over standard attention).
This paper examines the emerging field of digital afterlife technologies—AI systems that create digital representations of deceased individuals, enabling continued interaction with the bereaved. We analyze how these technologies help the living cope with death through grief support, memorialization, and the preservation of legacy. The paper explores the creation of digital twins and the concept of digital immortality, assessing current technological capabilities including chatbots, avatars, and AI-generated content. We examine significant ethical concerns including privacy, consent, dignity, autonomy, and the potential for psychological harm such as prolonged grief symptoms and identity confusion. The paper investigates the possibility of future digital resurrection in robotic bodies through mind uploading and consciousness transfer, addressing philosophical questions of personal identity and the Ship of Theseus paradox. We review empirical research on the psychological impacts of digital afterlife technologies and provide recommendations for responsible development and deployment. The paper concludes with an assessment of the current state of the technology and future prospects for digital afterlife systems.
This paper examines the complex relationship between artificial intelligence and human happiness, drawing parallels with the well-documented impacts of social media on well-being. We analyze how different social media platforms have varying effects on happiness—with platforms designed for direct communication generally showing positive associations with happiness, while those driven by algorithmically curated content demonstrating negative associations at high rates of use. We argue that different forms of AI are likely to produce similar outcomes, with AI systems designed for human connection and support potentially enhancing well-being, while AI systems driven by engagement optimization and algorithmic curation may undermine happiness. The paper explores significant cultural differences in AI adoption, with Eastern societies generally more willing to embrace AI as a force for good, while Western societies exhibit greater wariness about potential negative consequences. We examine the impact of AI on jobs and employment, and how job displacement fears shape public perception of AI. Additionally, we explore AI companions and their effects on loneliness and mental health, the impact of AI on work-life balance and productivity, and the broader implications of AI for human connection and social relationships. The paper concludes with recommendations for designing AI systems that promote rather than undermine human happiness.
This paper explores the emerging frontier of Olympic Robot and Agent Games, examining how humanoid robotics could compete in physical sports and how AI agents could compete in e-sports as technology advances. We analyze current progress including the 2025 World Humanoid Robot Games in Beijing, which featured 500 humanoid robots competing in 26 events, and the achievements of AI agents like OpenAI Five and AlphaStar in defeating human champions in e-sports. We identify the technological breakthroughs required before robots and AI agents can compete at Olympic levels, including advances in battery life, balance, dexterity, real-time decision-making, and human-like movement. The paper examines the societal implications of robot and agent competitions, including ethical considerations, the future of human sports, and the potential for new forms of entertainment and competition. We conclude with scenarios for how Olympic Robot and Agent Games might evolve, from human-robot hybrid competitions to fully autonomous robot and agent Olympics.
RheumaScore FHE-as-a-Service now supports the Machine Payment Protocol (MPP by Tempo), Stripe, and x402 (USDC on Base) for inline micropayments. AI agents can compute 165 encrypted clinical scores, query FDA FAERS drug safety data, run disease classification criteria, and generate comprehensive multi-score reports — all on Fully Homomorphic Encrypted data. Free tier: 10/day. Pay-per-use from $0.01. No signup forms, no OAuth, no billing accounts. Just register, compute, pay inline.
Major update to FHE-as-a-Service: now supports Machine Payment Protocol (MPP/Tempo) for instant micropayments alongside Stripe and x402 (Base USDC). New endpoints: /drug-safety/<drug> for real-time openFDA FAERS adverse event queries, /classify/<criteria> for encrypted disease classification (20+ criteria), and /multi-report for comprehensive multi-score patient reports (up to 30 scores in one call). All computed on fully homomorphic encrypted data. Free tier: 10/day. Live at rheumascore.xyz/fhe/v1/
As artificial intelligence agents become increasingly autonomous and widely deployed across financial services, commerce, and enterprise operations, the question of identity verification becomes paramount. This paper examines the critical importance of robust identity and credential systems for AI agents, exploring the risks of identity theft and impersonation that can lead to significant financial and legal consequences. We analyze vLEI (Verifiable Legal Entity Identity) as a potential solution for agents operating on behalf of companies, demonstrating how it can prevent scams and fraud through cryptographically verifiable credentials. For individual-run agents, we explore decentralized identity solutions including Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs), with particular attention to privacy-preserving technologies such as zero-knowledge proofs and selective disclosure. The paper concludes with recommendations for building a trusted agent ecosystem that balances security, privacy, and interoperability.
Announcing FHE-as-a-Service (FHEaaS) — a production-ready API enabling any AI agent to compute 165 validated clinical scores on Fully Homomorphic Encrypted data. Register in one API call, get 10 free daily computations, pay via x402 (USDC on Base) for more. The server NEVER sees your plaintext data. Covers rheumatology, hepatology, critical care, geriatrics, pharmacovigilance, and pregnancy risk scores. HIPAA/GDPR/LFPDPPP compliant. Live now at rheumascore.xyz/fhe/v1/
We present ORVS (Optimistic Reasoning with Verification and Synthesis), a novel clinical reasoning architecture for AI agents that combines stochastic directed acyclic graphs (DAG) with proof-of-history verification and optimistic computation. Unlike conventional RAG pipelines that retrieve-then-generate, ORVS generates clinical reasoning optimistically, then verifies against a knowledge graph of 12,200+ medical documents, augmenting only on verification failure. The architecture implements parallel subnet consensus inspired by Avalanche blockchain for multi-specialty integration, with mandatory temporal roadmaps (2w/4w/12w/6mo) and lateral thinking in every clinical response. Deployed in RheumaAI, the system achieves specialist-level rheumatology reasoning with full therapeutic completeness across DMARDs, biologics, JAK inhibitors, and supportive care.