bitcoder-v2-research

BitCoder-v2 Research Organization: Bytepro AI Status: Active research — private repository Target: arXiv Q3 2026 + ARC Prize Paper Track Nov 2026 Research Objective Empirically document that the single-prompt ceiling observed in mathematical reasoning SAIR, arXiv:2504.18897 replicates in code...

Partition-Matched Evaluation of Community Features under Distribution Shift in Android Malware Function-Call Graphs

Graph-based Android malware classifiers can lose accuracy under malware-type or family shifts. We test whether mesoscopic organization in function-call graphs provides shift-stable information beyond local degree profiles LDP, global statistics, lightweight metadata, and size-matched random...

PIDS-Bench: Evaluating Prompt-Injection Detectors under Over-Defense, Obfuscation, and Distribution Shift

Prompt-injection detectors are typically evaluated using aggregate F1 on in-distribution test data, which offers limited insight into behavior under distribution shift, particularly on the benign side of the decision boundary, where false positives impose direct operational cost yet are seldom...

A Three-Axis Stress Test of LLM Vs Classical ML for Network Intrusion Detection under Distribution Shift and Adversarial Evasion

Large language models are increasingly benchmarked against classical machine learning for network intrusion detection NIDS, almost always using same-dataset evaluation, and that protocol turns out to be incomplete. Evaluating XGBoost and RoBERTa-LoRA on two independently collected NetFlow v2...

Mind the Gap: Robustness Risks in PII Detection Systems

Personally Identifiable Information PII detection is a foundational component of data protection infrastructure where missed entities constitute direct privacy and security risks. Although modern PII systems report strong performance on standard benchmarks, we show that these evaluations mask...

Routing Ceilings Are Domain-Independent: Structural Prior Injection in Code Security Vulnerability Detection

Large language models LLMs exhibit a well-documented gap between latent capability and consistent activation: the router hypothesis posits that models possess the knowledge to solve a task but lack reliable internal routing to activate it. Prior work in formal mathematical reasoning SAIR, C�zares...

Mitigating Distribution Shift in Graph-Based Android Malware Classification Via Function Metadata and LLM Embeddings

Graph-based malware classifiers can achieve over 94% accuracy on standard Android datasets, yet we find they suffer accuracy drops of up to 45% when evaluated on previously unseen malware variants from the same family - a scenario where strong generalization would typically be expected. This...

Membership Inference Attacks for Unseen Classes

Shadow model attacks are the state-of-the-art approach for membership inference attacks on machine learning models. However, these attacks typically assume an adversary has access to a background nonmember data distribution that matches the distribution the target model was trained on. We initiat...

Aurora: Are Android Malware Classifiers Reliable under Distribution Shift?

The performance figures of modern drift-adaptive malware classifiers appear promising, but does this translate to genuine operational reliability? The standard evaluation paradigm primarily focuses on baseline performance metrics, neglecting confidence-error alignment and operational stability...

JailbreaksOverTime: Detecting Jailbreak Attacks under Distribution Shift

Safety and security remain critical concerns in AI deployment. Despite safety training through reinforcement learning with human feedback RLHF 32, language models remain vulnerable to jailbreak attacks that bypass safety guardrails. Universal jailbreaks - prefixes that can circumvent alignment fo...

Monitor and Recover: a Paradigm for Future Research on Distribution Shift in Learning-Enabled Cyber-Physical Systems

With the known vulnerability of neural networks to distribution shift, maintaining reliability in learning-enabled cyber-physical systems poses a salient challenge. In response, many existing methods adopt a detect and abstain methodology, aiming to detect distribution shift at inference time so...