cs papers | Gist.Science

What Makes a Review Read as Positive or Negative? Linguistic Signatures of Valence in Online Consumer and Cultural-Product Reviews

This study demonstrates that while sentiment intensity, readability, and lexical diversity reliably distinguish positive from negative reviews across film and consumer-electronics domains, affective punctuation exhibits contradictory valence signals between categories, highlighting the critical need for domain-specific calibration in automated sentiment analysis systems.

Senthil kumar Anantharaman2026-07-22

💻 computer science

Temporal Reliability of ChatGPT-Generated Bilingual Texts: Implications for AI-Assisted Language Learning and Multilingual Information Access

This study evaluates the temporal reliability of four successive ChatGPT versions in bilingual translation across finance, technology, and public policy domains, revealing that model updates do not guarantee consistent improvements in semantic accuracy or readability and instead exhibit diverging performance trajectories that necessitate continuous, domain-specific evaluations for AI-assisted language applications.

Zhihan Fu2026-07-22

💻 computer science

Computer Vision - Model evaluation for Wildlife Re-Identification with Small Datasets

This study demonstrates that for wildlife re-identification with small datasets, using a frozen large foundation model like DINOv2-Giant to extract features and train a lightweight metric-learning head can outperform fine-tuned specialized models, offering a reproducible, low-overhead framework for conservationists working with pattern-bearing species.

Rommel Sharma2026-07-22

💻 computer science

Preserving Myanmar’s Ancient Heritage: A Novel Dataset and ResNet Architecture for Pyu Character Recognition

This paper addresses the critical lack of computational resources for Myanmar's ancient Pyu script by introducing the first publicly available, systematically curated handwritten dataset of 33 consonants and validating a modified ResNet-18 architecture as a foundational benchmark for future digital preservation and epigraphic research.

Khant Sint Heinn2026-07-22

💻 computer science

DERMA-Agent: An Agentic Framework for Prognostic Discovery in Pan-Cancer Pathology

DERMA-Agent is a novel agentic framework that leverages multimodal foundation models and dynamic statistical execution to autonomously generate and validate morphomolecular prognostic signatures from pan-cancer whole slide images, addressing the limitations of static supervised models through iterative hypothesis discovery while maintaining rigorous safety controls in a retrospective research setting.

Gurumurthy Swaminathan2026-07-22

💻 computer science

Privacy-Preserving Keystroke Dynamics Authentication Using CKKS Homomorphic Encryption

This paper presents a privacy-preserving keystroke dynamics authentication system implemented using the CKKS homomorphic encryption scheme, which enables secure template matching via encrypted squared Euclidean distance calculations while ensuring biometric vectors remain confidential throughout the process.

Nipun Mahaarachchi2026-07-22

💻 computer science

Learning What to Think: Continual and Open-Ended Thinking Activity Scheduling for Autonomous Robots

This paper proposes a continual and open-ended framework that learns to schedule autonomous robots' thinking activities based on historical data and environmental context, significantly improving task success rates and cross-task generalization while reducing redundant LLM calls and operational violations.

Su Hong2026-07-22

💻 computer science

Predictable Emergence: An Empirical Analysis of Whether Sharp Capability Jumps Follow from Smooth Per-Token Scaling Laws

This paper demonstrates that the sharp "emergent" jumps in exact-match accuracy observed in multi-digit integer addition are not genuine discontinuities in model capability but are instead fully predictable artifacts resulting from smooth, power-law improvements in per-token accuracy compounded by the nonlinearity of requiring all digits to be correct.

Alexander Memming2026-07-22

💻 computer science

The Three-Force Phase Diagram of Mixture-of Experts: Gradient Alignment Theory and Causal Veri cation on Mixtral 8×7B

This paper introduces the cross-expert gradient alignment metric (Γ) to demonstrate that standard Mixture-of-Experts training, particularly in Mixtral 8×7B, is fundamentally driven by a structural zero-sum competition caused by the combined negative effects of softmax coupling and top-k normalization, which creates a persistent negative alignment attractor that can only be overcome by removing these specific architectural constraints.

庆君张2026-07-22

💻 computer science

An Effective Inter and Intra-Layerdependencies Based Communitydetection Model Using S-NOA and S2ASD-Munet.

This paper proposes a novel community detection model that leverages the Skellam Nutcracker Optimization Algorithm (S-NOA) and a SwishSERF Attention Spatial Drop-Macro Unit CNN (S2ASD-MUNet) to effectively analyze inter and intra-layer dependencies, achieving a high modularity score of 0.962.

Dheeraj Kumar Dubey, John Paul Martin, Keshab Nath2026-07-22

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