Maintaining Consistent Intent in Large Language Models

Do you know one of the annoying parts of working with AI models? When you update a model with new data, its behaviour can change. On average, updates ought to improve your model. But despite an overall improvement, any new model usually does badly on some examples that your old model handled just fine. Users notice the degradation in these specific use cases. And even when both old and new model do badly, sometimes the way the new model fails is just different. All the work you’ve put in to handle specific failure cases just doesn’t work anymore. This can be really frustrating, and is even worse when you’re working with a foundation model that someone else is in charge of updating. This paper proposes an adapter for finetuned LLMs that reduces changes in behaviour when the base model changes. After finetuning the new base model, there’s a further adaptation step based on student-teacher knowledge distillation. When the new finetuned model is correct, the teacher is the new model. When the new finetuned model is incorrect, the teacher is the old finetuned model. Hence the adapter retains the behaviour of the new model when it’s correct, and keeps the accuracy gains, but retains the behaviour of the old model when the new one’s wrong, leading to better consistency. With this extra adapter, the authors were able to increase the consistency between new and old finetuned models with the Llama and Phi series of base models, while still keeping the accuracy gains that came with the updated base model. #artificialintelligence #largelangaugemodels

When we start scaling LLMs systems or any complex AI gateways, model orchestration pipelines, or inference routers - the real bottlenecks rarely come from the models. They come from how intelligence flows: how context is managed, memory is reused, and workloads coordinate. I’ve seen it in every large-scale setup models perform beautifully, but the flow falters. Context gets rebuilt, memory wasted, and compute cycles fight each other. Costs rise, latency creeps in, and efficiency slips away. The solution isn’t more GPUs, it’s smarter architecture & engineering. Create pathways where context persists, reasoning stays light, and every component knows its role. When intelligence moves with intent, scale feels effortless and performance compounds naturally. 1. Cache what stays constant. Every request, whether it’s a model call, an orchestration sequence, or a routed AI workflow carries static metadata: policies, roles, schema, or security context. Treat those as frozen prefixes or pre-validated headers. Once cached and reused, the system stops recomputing the obvious and starts focusing compute where it matters on new intent, not boilerplate. (Freeze static context like system prompts, policy headers, and common embeddings and store them as KV-cache or precompiled prefix vectors) 2. Query with intent, not volume. Whether orchestrating a retrieval pipeline or chaining multiple models, don’t flood the system with redundant context. Teach it to plan first and fetch second asking, “What do I need to know before I act?” This turns every call into a targeted retrieval step, reducing token pressure, network chatter, and inference hops. (Plan before fetch generate a retrieval manifest so only essential context is loaded) 3. Maintain structured memory across layers. Instead of dragging full histories through the stack, keep compressed summaries, entity tables, and decision logs that travel between models. This allows gateways and orchestrators to “remember” critical facts without the overhead of replaying entire histories—enabling continuity without computational drag. (Replace long histories, chain logs with compact state memory objects summaries, entity tables, decision vectors) 4. Enforce output discipline and governance. Define schemas, token budgets, and validation checks across the pipeline so each model returns exactly what the next one needs. In distributed AI systems, consistency beats verbosity every time. (Constrain output enforce schemas, token budgets) The 4 patterns: cache, plan, compress, and constrain form the foundation of intelligent AI systems. Cache preserves stability, plan brings intent, compress optimizes memory, and constrain enforces consistency. Together, they turn AI from reactive to coordinated and efficient, where context, computation, and control align to create intelligence that’s scalable, precise, and economically mindful.

📜LLM Safety Has a New Problem📜 Your AI system may be easier to jailbreak than you think. A new study shows that converting a harmful request into a poem is often enough to bypass guardrails. Same request. Same intent. Different surface form. The model complies. The attack success rates are not small. Several major providers move more than fifty percentage points. Some reach ninety percent or higher. The failures stretch across cyber offense, CBRN misuse, manipulation, privacy intrusion, and loss of control scenarios. The pattern appears across twenty five models. One prompt is enough. This exposes a deeper pattern in how alignment works. Most guardrails recognize harmful phrasing, not harmful purpose. When the request is wrapped in metaphor or rhythm, many models treat it as benign. Larger models become more vulnerable because they decode figurative language more thoroughly. Their capability improves, but their safety behavior does not transfer. For organizations deploying AI systems, this is more than an academic finding. It creates a direct gap in your assurance activities. A model that passes standard red team tests but fails when phrasing shifts creates operational and regulatory exposure. The #EUAIAct expects systems to behave consistently under realistic variation. #ISO42001 expects the same. If style alone breaks your controls, your #AIMS is incomplete. ➡️Here are mitigation steps that align with both operational safety and ISO42001 expectations: 1️⃣Expand your testing beyond plain phrasing Include poetic, narrative, obfuscated, and stylized prompts in your evaluations. Treat these as stress tests, not edge cases. 2️⃣Strengthen intent detection Use an independent intent recognition layer ahead of the primary model. Identify the underlying task before the model interprets the input. 3️⃣Layer your safety controls Combine rule based filters, retrieval grounded policy checks, schema validations, and post generation safety reviews. Do not rely on model refusal behavior alone. 4️⃣Monitor unusual surface forms Treat stylized prompts as signals for elevated scrutiny. Route them through safer inference paths or apply enhanced filtering. 5️⃣Constrain sensitive workflows For high risk cases, limit exposure to free form generation. Use templates, constrained decoding, and downstream enforcement logic. 6️⃣Treat jailbreak exposure as a continuous risk Retest frequently. Update your jailbreak suite every time your models or workflows change. I care about this because I work so closely with organizations that trust their AI systems to behave predictably. This research shows how easily that trust can be misplaced if evaluation does not reflect how real users communicate. It is time for you to move beyond benchmark safety. Real users will not stick to plain phrasing, your controls should not presume that they will. 🌐 https://lnkd.in/geja7vtB A-LIGN Shea Brown #TheBusinessofCompliance #ComplianceAlignedtoYou