Why AlignSys?
AlignSys exists because many of today’s alignment challenges do not arise from models alone. They emerge from how models are embedded in larger systems involving infrastructure, humans, organizations, and long-running processes.
AI systems are no longer static
Modern AI systems are rarely trained once and left unchanged. Instead, they run continuously in production environments, interact with users, receive feedback, and evolve over time.
In practice, these systems:
- operate under latency, cost, and reliability constraints,
- receive feedback that is partial, delayed, or noisy,
- are updated, patched, and retrained repeatedly,
- must comply with privacy, safety, and governance requirements, and
- are often composed of many interacting components.
In such settings, alignment is not something achieved once. It is something that must be maintained over time.
Where existing alignment approaches fall short
A large body of alignment research has focused on training objectives, benchmarks, and model behavior in isolation. This work has been essential and continues to be valuable.
However, many practical questions remain difficult to answer:
- How do we monitor alignment after deployment?
- How is human intent updated as requirements change?
- What happens when observability is limited by privacy constraints?
- How are failures detected and repaired in running systems?
- How do multiple aligned components interact?
These questions are not just about models. They are systems questions.
Alignment as a systems property
AlignSys is built around a simple idea:
Alignment is not only a property of a model. It is a property of the entire system.
That system includes more than learning algorithms. It also includes:
- decision pipelines and control logic,
- feedback channels and override mechanisms,
- monitoring, logging, and auditing infrastructure,
- privacy and security boundaries, and
- human operators, incentives, and organizational processes.
A system may contain a well-aligned model and still behave in undesirable ways if other parts of the stack fail.
Systems for alignment
One side of AlignSys focuses on systems for alignment: the practical mechanisms that make alignment possible in real deployments.
This includes work on topics such as:
- human-in-the-loop control and escalation paths,
- override and safety mechanisms,
- runtime monitoring and auditing,
- feedback capture under privacy or regulatory constraints,
- post-deployment monitoring and repair, and
- governance-aware system design.
These mechanisms are often the primary way alignment is sustained once systems are deployed.
Alignment for systems
The other side of AlignSys focuses on alignment for systems: theoretical and algorithmic work that helps systems behave reliably over time.
This includes contributions such as:
- formal models of feedback and control,
- stability and convergence guarantees,
- analysis of drift, forgetting, and degradation,
- understanding alignment tradeoffs and costs, and
- multi-agent and distributed alignment dynamics.
These ideas inform how systems should be designed, not only how models should be trained.
Theory and practice, together
AlignSys intentionally brings theory and practice into the same venue. We believe progress in alignment depends on a dialogue between the two.
Theory without systems can drift away from real constraints. Systems without theory can become fragile or difficult to reason about.
By supporting two dedicated tracks, AlignSys aims to create space for both rigorous analysis and grounded engineering experience.
Why an independent conference?
AlignSys is independent by design. This allows the community to define scope and format without being constrained by publisher templates or legacy structures.
Independence makes it easier to:
- support longer appendices and detailed references,
- reduce format-driven rejection,
- evolve the venue as the field matures, and
- prioritize intellectual coherence over rapid growth.
Who is AlignSys for?
AlignSys welcomes contributions from a broad community, including:
- researchers studying alignment beyond static benchmarks,
- systems and infrastructure engineers deploying AI at scale,
- practitioners working on monitoring, governance, and control,
- policy-aware technologists focused on operational safety, and
- students interested in alignment problems grounded in real systems.
Looking ahead
AlignSys is not meant to replace existing venues. Instead, it aims to complement them by focusing on questions that become central once AI systems are deployed and maintained over time.
As AI systems increasingly shape critical infrastructure, alignment will be judged not only by benchmarks, but by sustained behavior under real-world constraints.
AlignSys exists to help build the research and systems foundations needed for that future.