BRAC University

DNA language models usually tokenize, train and test on one strand, so a sequence and its reverse complement can get different predictions. MirrorDNA uses a symmetric router that cuts both strands at mirrored positions and proves the whole model is equivariant to the reverse complement. On 12 strand-symmetric Nucleotide Transformer tasks it reaches an MCC of 0.491 on both strands, against 0.152, 0.187 and 0.080 for NT-v2, DNABERT-2 and HyenaDNA.

DNA language models usually tokenize, train and test on one strand, so a sequence and its reverse complement can get different predictions. MirrorDNA uses a symmetric router that cuts both strands at mirrored positions and proves the whole model is equivariant to the reverse complement. On 12 strand-symmetric Nucleotide Transformer tasks it reaches an MCC of 0.491 on both strands, against 0.152, 0.187 and 0.080 for NT-v2, DNABERT-2 and HyenaDNA.

The work turns a descriptive layered trust model into an operational controller: a cross-layer synergy operator propagates prerequisite-layer deficits into dependent layers, a no-regret online estimator grounds per-layer weights in observed failures, and Trust-Gated Capability Control issues short-lived, revocable grants only when composite trust and the relevant prerequisite layers clear capability-specific thresholds; the authors prove and numerically confirm that a stealthy compromise inflating behavioral trust while degrading a prerequisite layer cannot escalate privilege and instead self-revokes within a few interactions.

The work turns a descriptive layered trust model into an operational controller: a cross-layer synergy operator propagates prerequisite-layer deficits into dependent layers, a no-regret online estimator grounds per-layer weights in observed failures, and Trust-Gated Capability Control issues short-lived, revocable grants only when composite trust and the relevant prerequisite layers clear capability-specific thresholds; the authors prove and numerically confirm that a stealthy compromise inflating behavioral trust while degrading a prerequisite layer cannot escalate privilege and instead self-revokes within a few interactions.

The work introduces Reflex-Guard, a lightweight locally running guardrail for LLM prompt safety that combines jailbreak-aware preprocessing, compact sentence-transformer embeddings, and seven fast binary classifiers, achieving 95.9% recall on harmful prompts at 37.6 ms end-to-end latency on a balanced dataset of 30,568 samples drawn from five complementary sources, faster than Llama Guard 2 (255 ms) and SafeDecoding (723 ms), detecting 100% of GCG suffix attacks and Base64-encoded prompts at the default threshold, while DrAttack structured prompts required lowering the threshold to 0.03 for optimal detection.

The work introduces Reflex-Guard, a lightweight locally running guardrail for LLM prompt safety that combines jailbreak-aware preprocessing, compact sentence-transformer embeddings, and seven fast binary classifiers, achieving 95.9% recall on harmful prompts at 37.6 ms end-to-end latency on a balanced dataset of 30,568 samples drawn from five complementary sources, faster than Llama Guard 2 (255 ms) and SafeDecoding (723 ms), detecting 100% of GCG suffix attacks and Base64-encoded prompts at the default threshold, while DrAttack structured prompts required lowering the threshold to 0.03 for optimal detection.

The work introduces Reflex-Guard, a lightweight locally running guardrail for LLM prompt safety that combines jailbreak-aware preprocessing, compact sentence-transformer embeddings, and seven fast binary classifiers, achieving 95.9% recall on harmful prompts at 37.6 ms end-to-end latency on a strategically balanced dataset of 30,568 samples drawn from five complementary sources, faster than Llama Guard 2 (255 ms) and SafeDecoding (723 ms), detecting 100% of GCG suffix attacks and Base64-encoded prompts at the default threshold, while DrAttack structured prompts required lowering the threshold to 0.03 for optimal detection.

The work introduces Reflex-Guard, a lightweight locally running guardrail for LLM prompt safety that combines jailbreak-aware preprocessing, compact sentence-transformer embeddings, and seven fast binary classifiers, achieving 95.9% recall on harmful prompts at 37.6 ms end-to-end latency on a strategically balanced dataset of 30,568 samples drawn from five complementary sources, faster than Llama Guard 2 (255 ms) and SafeDecoding (723 ms), detecting 100% of GCG suffix attacks and Base64-encoded prompts at the default threshold, while DrAttack structured prompts required lowering the threshold to 0.03 for optimal detection.